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1
Bae, Tae Wuk, Kee Koo Kwon, and Kyu Hyung Kim. "Vital Block and Vital Sign Server for ECG and Vital Sign Monitoring in a Portable u-Vital System." Sensors 20, no. 4 (February 17, 2020): 1089. http://dx.doi.org/10.3390/s20041089.
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An important function in the future healthcare system involves measuring a patient’s vital signs, transmitting the measured vital signs to a smart device or a management server, analyzing it in real-time, and informing the patient or medical staff. Internet of Medical Things (IoMT) incorporates information technology (IT) into patient monitoring device (PMD) and is developing traditional measurement devices into healthcare information systems. In the study, a portable ubiquitous-Vital (u-Vital) system is developed and consists of a Vital Block (VB), a small PMD, and Vital Sign Server (VSS), which stores and manages measured vital signs. Specifically, VBs collect a patient’s electrocardiogram (ECG), blood oxygen saturation (SpO2), non-invasive blood pressure (NiBP), body temperature (BT) in real-time, and the collected vital signs are transmitted to a VSS via wireless protocols such as WiFi and Bluetooth. Additionally, an efficient R-point detection algorithm was also proposed for real-time processing and long-term ECG analysis. Experiments demonstrated the effectiveness of measurement, transmission, and analysis of vital signs in the proposed portable u-Vital system.
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Randazzo, Vincenzo, Jacopo Ferretti, and Eros Pasero. "A Wearable Smart Device to Monitor Multiple Vital Parameters—VITAL ECG." Electronics 9, no. 2 (February 9, 2020): 300. http://dx.doi.org/10.3390/electronics9020300.
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Smart devices are more and more present in every aspect of everyday life. From smartphones, which are now like mini-computers, through systems for monitoring sleep or fatigue, to specific sensors for the recording of vital parameters. A particular class of the latter regards health monitoring. Indeed, through the use of such devices, several vital parameters can be acquired and automatically monitored, even remotely. This paper presents the second generation of VITAL-ECG, a smart device designed to monitor the most important vital parameters as a “one touch” device, anywhere, at low cost. It is a wearable device that coupled with a mobile app can track bio-parameters such as: electrocardiogram, SpO2, skin temperature, and physical activity of the patient. Even if it not yet a medical device, a comprehensive comparison with a golden standard electrocardiograph is presented to demonstrate the quality of the recorded signals and the validity of the proposed approach.
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Ko, Li-Wei, Yang Chang, Bo-Kai Lin, and Dar-Shong Lin. "Vital Signs Sensing Gown Employing ECG-Based Intelligent Algorithms." Biosensors 12, no. 11 (November 3, 2022): 964. http://dx.doi.org/10.3390/bios12110964.
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This study presents a long-term vital signs sensing gown consisting of two components: a miniaturized monitoring device and an intelligent computation platform. Vital signs are signs that indicate the functional state of the human body. The general physical health of a person can be assessed by monitoring vital signs, which typically include blood pressure, body temperature, heart rate, and respiration rate. The miniaturized monitoring device is composed of a compact circuit which can acquire two kinds of physiological signals including bioelectrical potentials and skin surface temperature. These two signals were pre-processed in the circuit and transmitted to the intelligent computation platform for further analysis using three algorithms, which incorporate R-wave detection, ECG-derived respiration, and core body temperature estimation. After the processing, the derived vital signs would be displayed on a portable device screen, including ECG signals, heart rate (HR), respiration rate (RR), and core body temperature. An experiment for validating the performance of the intelligent computation platform was conducted in clinical practices. Thirty-one participants were recruited in the study (ten healthy participants and twenty-one clinical patients). The results showed that the relative error of HR is lower than 1.41%, RR is lower than 5.52%, and the bias of core body temperature is lower than 0.04 °C in both healthy participant and clinical patient trials. In this study, a miniaturized monitoring device and three algorithms which derive vital signs including HR, RR, and core body temperature were integrated for developing the vital signs sensing gown. The proposed sensing gown outperformed the commonly used equipment in terms of usability and price in clinical practices. Employing algorithms for estimating vital signs is a continuous and non-invasive approach, and it could be a novel and potential device for home-caring and clinical monitoring, especially during the pandemic.
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Schmidt, Marcus, Johannes W. Krug, Andy Schumann, Karl-Jürgen Bär, and Georg Rose. "Estimation of a respiratory signal from a single-lead ECG using the 4th order central moments." Current Directions in Biomedical Engineering 1, no. 1 (September 1, 2015): 61–64. http://dx.doi.org/10.1515/cdbme-2015-0016.
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AbstractFor a variety of clinical applications like magnetic resonance imaging (MRI) the monitoring of vital signs is a common standard in clinical daily routine. Besides the electrocardiogram (ECG), the respiratory activity is an important vital parameter and might reveal pathological changes. Thoracic movement and the resulting impedance change between ECG electrodes enable the estimation of the respiratory signal from the ECG. This ECG-derived respiration (EDR) can be used to calculate the breathing rate without the need for additional devices or monitoring modules. In this paper a new method is presented to estimate the respiratory signal from a single-lead ECG. The 4th order central moments was used to estimate the EDR signal exploiting the change of the R-wave slopes induced by respiration. This method was compared with two approaches by analyzing the Fantasia database from www.physionet.org. Furthermore, the ECG signals of 24 healthy subjects placed in an 3 T MR-scanner were acquired.
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Gautam, Mayank Kumar, and Vinod Kumar Giri. "An Approach of Neural Network For Electrocardiogram Classification." APTIKOM Journal on Computer Science and Information Technologies 1, no. 3 (January 16, 2020): 119–27. http://dx.doi.org/10.34306/csit.v1i3.57.
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ECG is basically the graphical representation of the electrical activity of cardiac muscles duringcontraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to thisearly detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated bythe cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiologicalparameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG playsa vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of patternrecognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes ofpredefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generatedwaveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters suchas spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includesartificial neural network as a classifier for identifying the abnormalities of heart disease.
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Gautam, Mayank Kumar, and Vinod Kumar Giri. "An Approach of Neural Network For Electrocardiogram Classification." APTIKOM Journal on Computer Science and Information Technologies 1, no. 3 (November 1, 2016): 119–27. http://dx.doi.org/10.11591/aptikom.j.csit.120.
Full textAbstract:
ECG is basically the graphical representation of the electrical activity of cardiac muscles during contraction and release stages. It helps in determination of the cardiac arrhythmias in a well manner. Due to this early detection of arrhythmias can be done properly. In other words we can say that the bio-potentials generated by the cardiac muscles results in an electrical signal called Electro-cardiogram (ECG). It acts as a vital physiological parameter, which is being used exclusively to know the state of the cardiac patients. Feature extraction of ECG plays a vital role in the manual as well as automatic analysis of ECG. In this paper the study of the concept of pattern recognition of ECG is done. It refers to the classification of data patterns and characterizing them into classes of predefined set. The analysis ECG signal falls under the application of pattern recognition. The ECG signal generated waveform gives almost all information about activity of the heart. The ECG signal feature extraction parameters such as spectral entropy, Poincare plot and Lyapunov exponent are used for study in this paper .This paper also includes artificial neural network as a classifier for identifying the abnormalities of heart disease.
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Aditya Mahendra Oka, Gede, and Andjar Pudji. "Design of Vital Sign Monitor with ECG, BPM, and Respiration Rate Parameters." Indonesian Journal of electronics, electromedical engineering, and medical informatics 3, no. 1 (February 22, 2021): 34–38. http://dx.doi.org/10.35882/ijeeemi.v3i1.6.
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Vital sign monitor is a device used to monitor a patient's vital sign, in the form of a heartbeat, pulse, blood pressure, temperature of the heart's pulse form continuously. Condition monitoring in patients is needed so that paramedics know the development of the condition of inpatients who are experiencing a critical period. Electrocardiogram (ECG) is a physiological signal produced by the electrical activity of the heart. Recording heart activity can be used to analyze how the characteristics of the heart. By obtaining respiration from outpatient electrocardiography, which is increasingly being used clinically to practice to detect and characterize the abnormal occurrence of heart electrical behavior during normal daily activities. The purpose of this study is to determine that the value of the Repiration Rate is taken from ECG signals because of its solidity. At the peak of the R ECG it has several respiratory signals such as signals in fluctuations. An ECG can be used to determine breathing numbers. This module utilizes leads ECG signals to 1 lead, namely lead 2, respiration rate taken from the ECG, BPM in humans displayed on a TFT LCD. This research module utilizes the use of filters to obtain ECG signals, and respiration rates to display the results on a TFT LCD. This module has the highest error value of 0.01% compared to the Phantom EKG tool. So this module can be used for the diagnosis process.ECG, Respiration Rate, Filter
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Proffitt, A., and P. Rees. "The athletic ECG." Journal of The Royal Naval Medical Service 102, no. 1 (June 2016): 50–55. http://dx.doi.org/10.1136/jrnms-102-50.
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AbstractThe electrocardiogram (ECG) is the most frequently performed basic cardiology investigation. Correct interpretation of the ECG is vital, both to confirm acute diagnoses such as myocardial infarction, and in the elective setting to diagnose previous or underlying cardiac abnormalities. Normal electrocardiographic parameters for the multiple components of the ECG have been identified and are applied to the general population, but it is acknowledged that cardiac conditioning occurs with frequent and sustained aerobic exercise, in turn leading to physiological changes in the ECG. Service personnel may perform exercise at a level that leads to cardiac conditioning with associated ECG changes. This clinical review will briefly address the normal ECG and consider changes associated with aerobic cardiac conditioning. By identifying what constitutes physiological non-pathological changes in the athletic ECG, this clinical review aims to assist those who interpret ECGs in Service personnel.
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Kim, Ju-Yeon, Jae-Hyun Park, Se-Young Jang, and Jong-Ryul Yang. "Peak Detection Algorithm for Vital Sign Detection Using Doppler Radar Sensors." Sensors 19, no. 7 (April 1, 2019): 1575. http://dx.doi.org/10.3390/s19071575.
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An accurate method for detecting vital signs obtained from a Doppler radar sensor is proposed. A Doppler radar sensor can remotely obtain vital signs such as heartbeat and respiration rate, but the vital signs obtained by using the sensor do not show clear peaks like in electrocardiography (ECG) because of the operating characteristics of the radar. The proposed peak detection algorithm extracts the vital signs from the raw data. The algorithm shows the mean accuracy of 96.78% compared to the peak count from the reference ECG sensor and a processing time approximately two times faster than the gradient-based algorithm. To verify whether heart rate variability (HRV) analysis similar to that with an ECG sensor is possible for a radar sensor when applying the proposed method, the continuous parameter variations of the HRV in the time domain are analyzed using data processed with the proposed peak detection algorithm. Experimental results with six subjects show that the proposed method can obtain the heart rate with high accuracy but cannot obtain the information for an HRV analysis because the proposed method cannot overcome the characteristics of the radar sensor itself.
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Elangovan, Ramanujam, and Padmavathi S. "A Review on Time Series Motif Discovery Techniques an Application to ECG Signal Classification." International Journal of Artificial Intelligence and Machine Learning 9, no. 2 (July 2019): 39–56. http://dx.doi.org/10.4018/ijaiml.2019070103.
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Cardiovascular disease diagnosis from an ECG signal plays an important and significant role in the health care system. Recently, numerous researchers have developed an automatic time series-based multi-step diagnosis system for the fast and accurate diagnosis of ECG abnormalities. The multi-step procedure involves ECG signal acquisition, signal pre-processing, feature extraction, and classification. Among which, the feature extraction plays a vital role in the field of accurate diagnosis. The features may be different types such as statistical, morphological, wavelet or any other signal-based approach. This article discusses various time series motif-based feature extraction techniques with respect to a different dimension of ECG signal.
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Rapin, Michaël, Yves-Julien Regamey, and Olivier Chételat. "Common-mode rejection in the measurement of wearable ECG with cooperative sensors." at - Automatisierungstechnik 66, no. 12 (December 19, 2018): 1002–13. http://dx.doi.org/10.1515/auto-2018-0061.
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Abstract Recently, telemonitoring of vital signs has gained a lot of research interest. Especially for electrocardiogram (ECG), which is among the most frequently measured vital sign. However, the integration of classical ECG Holter in wearables is problematic since shielded cables and gel electrodes are required to get ECG signals of highest quality. We have recently introduced a novel sensing architecture based on active electrodes (so-called cooperative sensors) that significantly reduces the cabling complexity of the monitoring device. After briefly recalling the principle of cooperative sensors this paper details how they address rejection of common-mode voltage induced by electromagnetic disturbances. The proposed approach uses an auto-identification technique based on a continuous-time calibration of the sensor system and a digital control loop. To demonstrate the reliability of the proposed approach, a 12-lead ECG monitoring system was implemented with the new common-mode rejection method. Measurements on four healthy volunteers showed that the signal quality obtained with the cooperative-sensor system (using dry electrodes) is equivalent to the one measured with a gold standard medical device (using gel electrodes) in exercise stress tests.
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Khairuddin, A. M., Ku Azir K. N. F., and P. Eh Kan. "A general framework for improving electrocardiography monitoring system with machine learning." Bulletin of Electrical Engineering and Informatics 8, no. 1 (March 1, 2019): 261–68. http://dx.doi.org/10.11591/eei.v8i1.1400.
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As one of the most important health monitoring systems, electrocardiography (ECG) is used to obtain information about the structure and functions of the human heart for detecting and preventing cardiovascular disease. Given its important role, it is vital that the ECG monitoring system provides relevant and accurate information about the heart. Over the years, numerous attempts were made to design and develop more effective ECG monitoring system. Nonetheless, the literature reveals not only several limitations in conventional ECG monitoring system but also emphasizes on the need to adopt new technology such as machine learning to improve the monitoring system as well as its medical applications. This paper reviews previous works on machine learning to explain its key features, capabilities as well as presents a general framework for improving ECG monitoring system.
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Song, Guanghui, Jiajian Zhang, Dandan Mao, Genlang Chen, and Chaoyi Pang. "A Multimodel Fusion Method for Cardiovascular Disease Detection Using ECG." Emergency Medicine International 2022 (May 16, 2022): 1–10. http://dx.doi.org/10.1155/2022/3561147.
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Objective. Electrocardiogram (ECG) is an important diagnostic tool that has been the subject of much research in recent years. Owing to a lack of well-labeled ECG record databases, most of this work has focused on heartbeat arrhythmia detection based on ECG signal quality. Approach. A record quality filter was designed to judge ECG signal quality, and a random forest method, a multilayer perceptron, and a residual neural network (RESNET)-based convolutional neural network were implemented to provide baselines for ECG record classification according to three different principles. A new multimodel method was constructed by fusing the random forest and RESNET approaches. Main Results. Owing to its ability to combine discriminative human-crafted features with RESNET deep features, the proposed new method showed over 88% classification accuracy and yielded the best results in comparison with alternative methods. Significance. A new multimodel fusion method was presented for abnormal cardiovascular detection based on ECG data. The experimental results show that separable convolution and multiscale convolution are vital for ECG record classification and are effective for use with one-dimensional ECG sequences.
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Chhavi Saxena, Dr, Dr Avinash Sharma, Dr Rahul Srivastav, and Dr Hemant Kumar Gupta. "Denoising of Ecg Signals Using Fir & Iir Filter: a Performance Analysis." International Journal of Engineering & Technology 7, no. 4.12 (October 4, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i4.12.20982.
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Electrocardiogram (ECG) signal is the electrical recording of coronary heart activity. It is a common routine and vital cardiac diagnostic tool in which in electric signals are measured and recorded to recognize the practical status of heart, but ECG signal can be distorted with noise as, numerous artifacts corrupt the unique ECG signal and decreases it quality. Consequently, there may be a need to eliminate such artifacts from the authentic signal and enhance its quality for better interpretation. ECG signals are very low frequency signals of approximately 0.5Hz-100Hz and digital filters are used as efficient approach for noise removal of such low frequency signals. Noise may be any interference because of movement artifacts or due to power device that are present wherein ECG has been taken. Consequently, ECG signal processing has emerged as a common and effective tool for research and clinical practices. This paper gives the comparative evaluation of FIR and IIR filters and their performances from the ECG signal for proper understanding and display of the ECG signal.
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Tseng, Kuo-Kun, Jiao Lo, Chih-Cheng Chen, Shu-Yi Tu, and Cheng-Fu Yang. "Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches." Sensors 18, no. 12 (November 26, 2018): 4138. http://dx.doi.org/10.3390/s18124138.
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Electrocardiograph (ECG) technology is vital for biometric security, and blood oxygen is essential for human survival. In this study, ECG signals and blood oxygen levels are combined to increase the accuracy and efficiency of human identification and verification. The proposed scheme maps the combined biometric information to a matrix and quantifies it as a sparse matrix for reorganizational purposes. Experimental results confirm a much better identification rate than in other ECG-related identification studies. The literature shows no research in human identification using the quantization sparse matrix method with ECG and blood oxygen data combined. We propose a multi-dimensional approach that can improve the accuracy and reduce the complexity of the recognition algorithm.
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Romelah, Kuspariyah. "PERBEDAAN TANDA- TANDA VITAL DAN EKG SEBELUM DAN SESUDAH REHABILITASI JANTUNG FASE 1 PADA PASIEN PENYAKIT JANTUNG KORONER." Media Husada Journal Of Nursing Science 2, no. 3 (November 25, 2021): 167–78. http://dx.doi.org/10.33475/mhjns.v2i3.68.
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ABSTRAK
Penyakit jantung koroner adalah penimbunan plak pada pembuluh darah koroner, sehingga menyebabkan arteri koroner menyempit atau tersumbat. Tujuan penelitian ini untuk menganalisis perbedaan tanda- tanda vital dan ekg sebelum dan sesudah rehabilitasi jantung fase 1 pada pasien penyakit jantung koroner. Desain penelitian ini one Group Pre dan Post Test Design. Pengambilan sampel menggunakan tehnik purposive sampling dengan jumlah sampel 32 responden pasien. Analisa data menggunakan uji Pairet T Sample test. Hasil penelitian yang menunjukkan normal sebelum rehabilitasi jantung fase 1 tekanan darah sistole 65,63%, tekanan darah diastole 78,12%, nadi 78,13%, respirasi 100%, suhu 87,5%, ekg 68,75%. Dan yang menunjukkan normal sesudah rehabilitasi jantung fase 1 yaitu tekanan darah sistole 75% , tekanan darah diastole 93,75%, nadi 68,75%, respirasi 68,75%, suhu 100% ekg 87,5%. Hasil uji statistik Pairet T Sample Test didapatkan 0,012 (< 0,05). Kesimpulannya adalah ada perbedaan tanda- tanda vital dan ekg sebelum dan sesudah rehabilitasi jantung fase 1 pada pasien penyakit jantung koroner di IPJT RSSA Malang.
Kata kunci : Rehabilitasi, Tanda- tanda vital, Penyakit Jantung Koroner
ABSTRACT
Coronary heart disease is the accumulation of plaque in the coronary arteries, causing the coronary arteries to become narrowed or blocked. The purpose of this study was to analyze the differences in vital signs and ECG before and after phase 1 cardiac rehabilitation in patients with coronary heart disease. The design of this research is one group pre and post test design. Sampling using purposive sampling technique with a sample of 32 patient respondents. Analysis of the data using the Pairet T Sample test. The results showed normal before cardiac rehabilitation phase 1 systolic blood pressure 65.63%, diastolic blood pressure 78.12%, pulse 78.13%, respiration 100%, temperature 87.5%, ecg 68.75%. And what showed normal after phase 1 cardiac rehabilitation were systolic blood pressure 75%, diastolic blood pressure 93.75%, pulse 68.75%, respiration 68.75%, temperature 100% ecg 87.5%. The results of the Pairet T Sample Test statistical test obtained 0.012 (<0.05). The conclusion is that there are differences in vital signs and ECG before and after phase 1 cardiac rehabilitation in coronary heart disease patients at IPJT RSSA Malang.
Key words : Rehabilitation, Vital signs, Coronary Heart Disease
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Wang, Zhi, Beihong Jin, Siheng Li, Fusang Zhang, and Wenbo Zhang. "ECG-grained Cardiac Monitoring Using UWB Signals." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 4 (December 21, 2022): 1–25. http://dx.doi.org/10.1145/3569503.
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With the development of wireless sensing, researchers have proposed many contactless vital sign monitoring systems, which can be used to monitor respiration rates, heart rates, cardiac cycles and etc. However, these vital signs are ones of coarse granularity, so they are less helpful in the diagnosis of cardiovascular diseases (CVDs). Considering that electrocardiogram (ECG) is an important evidence base for the diagnoses of CVDs, we propose to generate ECGs from ultra-wideband (UWB) signals in a contactless manner as a fine-grained cardiac monitoring solution. Specifically, we analyze the properties of UWB signals containing heartbeats and respiration, and design two complementary heartbeat signal restoration methods to perfectly recover heartbeat signal variation. To establish the mapping between the mechanical activity of the heart sensed by UWB devices and the electrical activity of the heart recorded in ECGs, we construct a conditional generative adversarial network to encode the mapping between mechanical activity and electrical activity and propose a contrastive learning strategy to reduce the interference from noise in UWB signals. We build the corresponding cardiac monitoring system named RF-ECG and conduct extensive experiments using about 120,000 heartbeats from more than 40 participants. The experimental results show that the ECGs generated by RF-ECG have good performance in both ECG intervals and morphology compared with the ground truth. Moreover, diseases such as tachycardia/bradycardia, sinus arrhythmia, and premature contractions can be diagnosed from the ECGs generated by our RF-ECG.
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Nayan, Nazrul Anuar, and Hafifah Ab Hamid. "Evaluation of patient electrocardiogram datasets using signal quality indexing." Bulletin of Electrical Engineering and Informatics 8, no. 2 (June 1, 2019): 519–26. http://dx.doi.org/10.11591/eei.v8i2.1289.
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Electrocardiogram (ECG) is widely used in the hospital emergency rooms for detecting vital signs, such as heart rate variability and respiratory rate. However, the quality of the ECGs is inconsistent. ECG signals lose information because of noise resulting from motion artifacts. To obtain an accurate information from ECG, signal quality indexing (SQI) is used where acceptable thresholds are set in order to select or eliminate the signals for the subsequent information extraction process. A good evaluation of SQI depends on the R-peak detection quality. Nevertheless, most R-peak detectors in the literature are prone to noise. This paper assessed and compared five peak detectors from different resources. The two best peak detectors were further tested using MIT-BIH arrhythmia database and then used for SQI evaluation. These peak detectors robustly detected the R-peak for signals that include noise. Finally, the overall SQI of three patient datasets, namely, Fantasia, CapnoBase, and MIMIC-II, was conducted by providing the interquartile range (IQR) and median SQI of the signals as the outputs. The evaluation results revealed that the R-peak detectors developed by Clifford and Behar showed accuracies of 98% and 97%, respectively. By introducing SQI and choosing only high-quality ECG signals, more accurate vital sign information will be achieved.
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Ma, Jing, Jun Xu, Hai Bo Xu, Yu Wang, and Sheng Xu Yin. "Design of ECG Signal Acquisition and Processing Circult." Applied Mechanics and Materials 236-237 (November 2012): 856–61. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.856.
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ECG signal is, as a vital method performed on the heart study and clinical diagnosis of cardiovascular diseases, an important human physiological signal, containing the human cardiac conduction system of physiological and pathological information. Aiming at the weak low frequency characteristic of ECG signals, the core circuit based on the AD620 and LM324 amplifier is given. After analyzing the major components of the ECG signal and the frequency range of interference, weak ECG signal collected by the electrodes is amplified by the preamplifier circuit, and the interference then is wiped out by using a low-pass filer, a high-pass filer, 50Hz notch filer and back amplifier circuit, finally a right wave of ECG is received. The characteristics of the system features the merits of high input impedance, high CMRR, low noise, less excursion and high SNR(signal to noise ratio), low cost and so on.
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Srivastava, Rohini, Basant Kumar, Fayadh Alenezi, Adi Alhudhaif, Sara A. Althubiti, and Kemal Polat. "Automatic Arrhythmia Detection Based on the Probabilistic Neural Network with FPGA Implementation." Mathematical Problems in Engineering 2022 (March 22, 2022): 1–11. http://dx.doi.org/10.1155/2022/7564036.
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This paper presents a prototype implementation of arrhythmia classification using Probabilistic neural network (PNN). Arrhythmia is an irregular heartbeat, resulting in severe heart problems if not diagnosed early. Therefore, accurate and robust arrhythmia classification is a vital task for cardiac patients. The classification of ECG has been performed using PNN into eight ECG classes using a unique combination of six ECG features: heart rate, spectral entropy, and 4th order of autoregressive coefficients. In addition, FPGA implementation has been proposed to prototype the complete system of arrhythmia classification. Artix-7 board has been used for the FPGA implementation for easy and fast execution of the proposed arrhythmia classification. As a result, the average accuracy for ECG classification is found to be 98.27%, and the time consumed in the classification is found to be 17 seconds.
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Bao, Xinqi, Aimé Kingwengwe Abdala, and Ernest Nlandu Kamavuako. "Estimation of the Respiratory Rate from Localised ECG at Different Auscultation Sites." Sensors 21, no. 1 (December 25, 2020): 78. http://dx.doi.org/10.3390/s21010078.
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The respiratory rate (RR) is a vital physiological parameter in prediagnosis and daily monitoring. It can be obtained indirectly from Electrocardiogram (ECG) signals using ECG-derived respiration (EDR) techniques. As part of the study in designing an early cardiac screening system, this work aimed to study whether the accuracy of ECG derived RR depends on the auscultation sites. Experiments were conducted on 12 healthy subjects to obtain simultaneous ECG (at auscultation sites and Lead I as reference) and respiration signals from a microphone close to the nostril. Four EDR algorithms were tested on the data to estimate RR in both the time and frequency domain. Results reveal that: (1) The location of the ECG electrodes between auscultation sites does not impact the estimation of RR, (2) baseline wander and amplitude modulation algorithms outperformed the frequency modulation and band-pass filter algorithms, (3) using frequency domain features to estimate RR can provide more accurate RR except when using the band-pass filter algorithm. These results pave the way for ECG-based RR estimation in miniaturised integrated cardiac screening device.
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Amhia, Hemant, and A. K. Wadhwani. "Designing an Optimum and Reduced Order Filter for Efficient ECG QRS Peak Detection and Classification of Arrhythmia Data." Journal of Healthcare Engineering 2021 (December 22, 2021): 1–17. http://dx.doi.org/10.1155/2021/6542290.
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Electrocardiogram (ECG) is commonly used biological signals that show an important role in cardiac analysis. The interpretation and acquisition of QRS complex are significant measures of ECG data dispensation. The R wave has a vital character in the analysis of cardiac rhythm irregularities as well as in the determination of heart rate variability (HRV). This manuscript is proposed to design a new artificial-intelligence-based approach of QRS peak detection and classification of the ECG data. The design of reduced order IIR filter is proposed for the low pass smoothening of the ECG signal data. The min-max optimization is used for optimizing the filter coefficient to design the reduced order filter. In this research paper, elimination of baseline wondering and the power line interferences from the ECG signal is of main attention. The result presented that the accuracy is increased by around 13% over the basic Pan–Tompkins method and around 8% over the existing FIR-filter-based classification rules.
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Hui, Xiaonan, and Edwin C. Kan. "No-touch measurements of vital signs in small conscious animals." Science Advances 5, no. 2 (February 2019): eaau0169. http://dx.doi.org/10.1126/sciadv.aau0169.
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Measuring the heartbeat and respiration of small conscious animals is important for assessing their health and behavior, but present techniques such as electrocardiogram (ECG), ultrasound, and auscultation rely on close skin contact with the animal. These methods can also require surface preparation, cause discomfort or stress to animals, and even require anesthetic administration, especially for birds, reptiles, and fish. Here, we show that radio frequency near-field coherent sensing (NCS) can provide a new solution to animal vital sign monitoring while ensuring minimal pain and distress. We first benchmarked NCS with synchronous ECG on an anesthetized rat. NCS was then applied to monitor a conscious hamster from outside its cage, and was further extended to a parakeet, Russian tortoise, and betta fish in a noninvasive manner. Our system can revolutionize vital sign monitoring of small conscious animals in their laboratory living quarters or natural habitats.
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von Rosenberg, Wilhelm, Theerasak Chanwimalueang, Valentin Goverdovsky, Nicholas S. Peters, Christos Papavassiliou, and Danilo P. Mandic. "Hearables: feasibility of recording cardiac rhythms from head and in-ear locations." Royal Society Open Science 4, no. 11 (November 2017): 171214. http://dx.doi.org/10.1098/rsos.171214.
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Mobile technologies for the recording of vital signs and neural signals are envisaged to underpin the operation of future health services. For practical purposes, unobtrusive devices are favoured, such as those embedded in a helmet or incorporated onto an earplug. However, these locations have so far been underexplored, as the comparably narrow neck impedes the propagation of vital signals from the torso to the head surface. To establish the principles behind electrocardiogram (ECG) recordings from head and ear locations, we first introduce a realistic three-dimensional biophysics model for the propagation of cardiac electric potentials to the head surface, which demonstrates the feasibility of head-ECG recordings. Next, the proposed biophysics propagation model is verified over comprehensive real-world experiments based on head- and in-ear-ECG measurements. It is shown both that the proposed model is an excellent match for the recordings, and that the quality of head- and ear-ECG is sufficient for a reliable identification of the timing and shape of the characteristic P-, Q-, R-, S- and T-waves within the cardiac cycle. This opens up a range of new possibilities in the identification and management of heart conditions, such as myocardial infarction and atrial fibrillation, based on 24/7 continuous in-ear measurements. The study therefore paves the way for the incorporation of the cardiac modality into future ‘hearables’, unobtrusive devices for health monitoring.
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Nguyen Thi, Ngoc Anh, Hyung-Jeong Yang, SunHee Kim, and Luu Ngoc Do. "A Harmonic Linear Dynamical System for Prominent ECG Feature Extraction." Computational and Mathematical Methods in Medicine 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/761536.
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Unsupervised mining of electrocardiography (ECG) time series is a crucial task in biomedical applications. To have efficiency of the clustering results, the prominent features extracted from preprocessing analysis on multiple ECG time series need to be investigated. In this paper, a Harmonic Linear Dynamical System is applied to discover vital prominent features via mining the evolving hidden dynamics and correlations in ECG time series. The discovery of the comprehensible and interpretable features of the proposed feature extraction methodology effectively represents the accuracy and the reliability of clustering results. Particularly, the empirical evaluation results of the proposed method demonstrate the improved performance of clustering compared to the previous main stream feature extraction approaches for ECG time series clustering tasks. Furthermore, the experimental results on real-world datasets show scalability with linear computation time to the duration of the time series.
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Saha, Partho Kumar, and Aisha Singh. "IoT Based Smart ECG Monitoring System." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (February 28, 2023): 1483–90. http://dx.doi.org/10.22214/ijraset.2023.49303.
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Abstract: Public healthcare has been paid increasing attention given the exponential growth human population and medical expenses. It is well known that an effective health monitoring system can detect abnormalities of health conditions in time and make diagnoses according to the gleaned data. As a vital approach to diagnosing heart diseases, ECG monitoring is widely studied and applied. However, nearly all existing portable ECG monitoring systems cannot work without a mobile application, which is responsible for data collection and display. In this project, we propose a new method for ECG monitoring based on Internet-of-Things (IoT) techniques. ECG data are gathered using a wearable monitoring node and are transmitted directly to the IoT cloud using Wi-Fi. Both the HTTP and MQTT protocols are employed in the IoT cloud in order to provide visual and timely ECG data to users. Nearly all smart terminals with a web browser can acquire ECG data conveniently, which has greatly alleviated the cross-platform issue. Experiments are carried out on healthy volunteers in order to verify the reliability of the entire system. Experimental results reveal that the proposed system is reliable in collecting and displaying real-time ECG data, which can aid in the primary diagnosis of certain heart diseases.
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Patel, Vandana, and Ankit Shah. "Denoising electrocardiogram signals using multiband filter and its implementation on FPGA." Serbian Journal of Electrical Engineering 19, no. 2 (2022): 115–28. http://dx.doi.org/10.2298/sjee2202115p.
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The electrocardiogram (ECG) signal carries vital information related to cardiac activities. While measuring ECG using electrodes, the signal is contaminated with powerline interference (PLI) from harmonics, baseline wandering (BW), motion artefacts (MA) and high frequency (HF) noise. The extraction of the ECG signal, without the loss of useful information from the noisy environment, is required. Therefore, the selection and implementation of an efficient filter design is proposed. The Finite Impulse Response (FIR)-based multiband needs separate digital filters, such as Lowpass, Highpass, and Bandstop Filter in cascade. The coefficients of the FIR multiband filter are optimised using a least squares optimisation method and realised in a direct form symmetrical structure. The capability of the proposed filter is evaluated on a Physionet ECG ID database, having records of inherent noisy ECG signals. The performance is also verified by measuring the power spectrum of the noisy and filtered ECG waveform. Also, the feasibility of the proposed multiband filter is investigated on Xilinx ISE and the design is implemented on a field programmable gate array (FPGA) platform. A low order simple multiband filter structure is designed and implemented on the reconfigurable FPGA device.
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Rajani, A., and V. Sandeep. "A Novel method of QRS Detection Using Adaptive Multilevel Thresholding with Statistical False Peak Elimination." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 1406–13. http://dx.doi.org/10.22214/ijraset.2022.46848.
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Abstract: Heart is the vital organ of a Human Body, because of its involvement in various works and processes in the entire body such as blood pumping etc., so recording a heart function is also a great thing, it is done through ECG signals. ECG signal records the electrical signals and activity of a Human Heart based on the electrical signals released by the Heart. ECG signal consists of PQRST waves, which are the reference points on an ECG signal. But, recording them is much easy than extracting and analyzing them, so, as to extract them, we are applying an advanced adaptive multi-level thresholding (AAMT) along with a selective statistical false peak elimination for the detection of QRS peaks of an ECG signal. Initially, median and moving average filters are applied for removing noise as well as terms. After AAMT is implemented on the complete dataset of ECG signals. Then selective statistical false peak elimination (SSFPE) is implemented for removing noise terms that might be missed out during filtering. At last, a search back stage will be implemented to search for low amplitude useful peaks.
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Wang, Zhelong, Cong Zhao, and Sen Qiu. "A system of human vital signs monitoring and activity recognition based on body sensor network." Sensor Review 34, no. 1 (January 14, 2014): 42–50. http://dx.doi.org/10.1108/sr-12-2012-735.
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Purpose – The purpose of this paper is to develop a health monitoring system that can measure human vital signs and recognize human activity based on body sensor network (BSN). Design/methodology/approach – The system is mainly composed of electrocardiogram (ECG) signal collection node, blood oxygen signal collection node, inertial sensor node, receiving node and upper computer software. The three collection nodes collect ECG signals, blood oxygen signals and motion signals. And then collected signals are transmitted wirelessly to receiving node and analyzed by software in upper computer in real-time. Findings – Experiment results show that the system can simultaneously monitor human ECG, heart rate, pulse rate, SpO2 and recognize human activity. A classifier based on coupled hidden Markov model (CHMM) is adopted to recognize human activity. The average recognition accuracy of CHMM classifier is 94.8 percent, which is higher than some existent methods, such as supported vector machine (SVM), C4.5 decision tree and naive Bayes classifier (NBC). Practical implications – The monitoring system may be used for falling detection, elderly care, postoperative care, rehabilitation training, sports training and other fields in the future. Originality/value – First, the system can measure human vital signs (ECG, blood pressure, pulse rate, SpO2, temperature, heart rate) and recognizes some specific simple or complex activities (sitting, lying, go boating, bicycle riding). Second, the researches of using CHMM for activity recognition based on BSN are extremely few. Consequently, the classifier based on CHMM is adopted to recognize activity with ideal recognition accuracies in this paper.
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Premkumar, M., S. Sathiyapriya, M. Arun, and Vikash Sachan. "Medical Signal Processing via Digital Filter and Transmission Reception Using Cognitive Radio Technology." Traitement du Signal 39, no. 4 (August 31, 2022): 1357–62. http://dx.doi.org/10.18280/ts.390429.
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This research paper provides a viable solution for processing noise affected Electrocardiogram (ECG) signal via digital filter and transmission of ECG signal and reception via cognitive radio (CR) technology. Health assessment signals such as ECG signal, Electroencephalogram (EEG) signal, Electromyogram (EMG) signal are vital for diagnosis and rehabilitation of human welfare among which ECG attains prime importance due to its information on heart functioning. However, electrocardiogram signals are prone to addition of noise such as power line noise 50 Hz mainly due to improper shielding which can lead to wrong interpretation, incorrect diagnosis and at times will eventually lead to loss of human life. On combining signal processing into medical applications misconceptions can be eliminated and diagnosis can be done effectively through a designed digital filter. Effect of noise can be cancelled in an ECG signal and by using cognitive radio technology ECG information can be transmitted to a medical physician mobile terminal for remedial measures relating to medical treatment. Simulation results are shown in matrix laboratory (MATLAB) for cancelling noise in an ECG signal having noise using a digital filter which is designed represented by its transfer function. Also, ECG signal is transmitted and received in a CR system where the metric of probability of error is obtained which can be useful for signal processing fraternity.
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Lu, Guo Hua, Fang Fang, Xi Jing Jing, Xiao Yu, and Jian Qi Wang. "A Contact-Free Monitor of Human’S Vital Signs." Applied Mechanics and Materials 138-139 (November 2011): 1063–66. http://dx.doi.org/10.4028/www.scientific.net/amm.138-139.1063.
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Heart rates and breathing rates are widely used to assess the health state of human in clinic. Tranditional method uses eletrodes or sensors touching the body to measure electrocardiography (ECG) and respiratory signals.A vital signs monitor via a micorwave sensor was disscused to contact-free measurement of the heart rate and breathing rate. Comparison of vital signs derived from the microwave sensor and tranditional contact monitor demonstrated that there were no significant differences between each other, which suggested the contact-free vital signs monitor may prove a practical alternative method to measure heart rate and breathing rate.
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Quiroz-Juárez, Mario Alan, Juan Alberto Rosales-Juárez, Omar Jiménez-Ramírez, Rubén Vázquez-Medina, and José Luis Aragón. "ECG Patient Simulator Based on Mathematical Models." Sensors 22, no. 15 (July 30, 2022): 5714. http://dx.doi.org/10.3390/s22155714.
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In this work, we propose a versatile, low-cost, and tunable electronic device to generate realistic electrocardiogram (ECG) waveforms, capable of simulating ECG of patients within a wide range of possibilities. A visual analysis of the clinical ECG register provides the cardiologist with vital physiological information to determine the patient’s heart condition. Because of its clinical significance, there is a strong interest in algorithms and medical ECG measuring devices that acquire, preserve, and process ECG recordings with high fidelity. Bearing this in mind, the proposed electronic device is based on four different mathematical models describing macroscopic heartbeat dynamics with ordinary differential equations. Firstly, we produce full 12-lead ECG profiles by implementing a model comprising a network of heterogeneous oscillators. Then, we implement a discretized reaction–diffusion model in our electronic device to reproduce ECG waveforms from various rhythm disorders. Finally, in order to show the versatility and capabilities of our system, we include two additional models, a ring of three coupled oscillators and a model based on a quasiperiodic motion, which can reproduce a wide range of pathological conditions. With this, the proposed device can reproduce around thirty-two cardiac rhythms with the possibility of exploring different parameter values to simulate new arrhythmias with the same hardware. Our system, which is a hybrid analog–digital circuit, generates realistic ECG signals through digital-to-analog converters whose amplitudes and waveforms are controlled through an interactive and friendly graphic interface. Our ECG patient simulator arises as a promising platform for assessing the performance of electrocardiograph equipment and ECG signal processing software in clinical trials. Additionally the produced 12-lead profiles can be tested in patient monitoring systems.
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Nayan, Nazrul Anuar, Rosmina Jaafar, and Nur Sabrina Risman. "Development of Respiratory Rate Estimation Technique Using Electrocardiogram and Photoplethysmogram for Continuous Health Monitoring." Bulletin of Electrical Engineering and Informatics 7, no. 3 (September 1, 2018): 487–94. http://dx.doi.org/10.11591/eei.v7i3.1244.
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Abnormal vital signs often predict a serious condition of acutely ill hospital patients in 24 hours. The notable fluctuations of respiratory rate (RR) are highly predictive of deteriorations among the vital signs measured. Traditional methods of detecting RR are performed by directly measuring the air flow in or out of the lungs or indirectly measuring the changes of the chest volume. These methods require the use of cumbersome devices, which may interfere with natural breathing, are uncomfortable, have frequently moving artifacts, and are extremely expensive. This study aims to estimate the RR from electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which consist of passive and non-invasive acquisition modules. Algorithms have been validated by using PhysioNet’s Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II)’s patient datasets. RR estimation provides the value of mean absolute error (MAE) for ECG as 1.25 bpm (MIMIC-II) and 1.05 bpm for the acquired data. MAE for PPG is 1.15 bpm (MIMIC-II) and 0.90 bpm for the acquired data. By using 1-minute windows, this method reveals that the filtering method efficiently extracted respiratory information from the ECG and PPG signals. Smaller MAE for PPG signals results from fewer artifacts due to easy sensor attachment for the PPG because PPG recording requires only one-finger pulse oximeter sensor placement. However, ECG recording requires at least three electrode placements at three positions on the subject’s body surface for a single lead (lead II), thereby increasing the artifacts. A reliable technique has been proposed for RR estimation.
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Rashkovska, Aleksandra, Matjaž Depolli, Ivan Tomašić, Viktor Avbelj, and Roman Trobec. "Medical-Grade ECG Sensor for Long-Term Monitoring." Sensors 20, no. 6 (March 18, 2020): 1695. http://dx.doi.org/10.3390/s20061695.
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The recent trend in electrocardiogram (ECG) device development is towards wireless body sensors applied for patient monitoring. The ultimate goal is to develop a multi-functional body sensor that will provide synchronized vital bio-signs of the monitored user. In this paper, we present an ECG sensor for long-term monitoring, which measures the surface potential difference between proximal electrodes near the heart, called differential ECG lead or differential lead, in short. The sensor has been certified as a class IIa medical device and is available on the market under the trademark Savvy ECG. An improvement from the user’s perspective—immediate access to the measured data—is also implemented into the design. With appropriate placement of the device on the chest, a very clear distinction of all electrocardiographic waves can be achieved, allowing for ECG recording of high quality, sufficient for medical analysis. Experimental results that elucidate the measurements from a differential lead regarding sensors’ position, the impact of artifacts, and potential diagnostic value, are shown. We demonstrate the sensors’ potential by presenting results from its various areas of application: medicine, sports, veterinary, and some new fields of investigation, like hearth rate variability biofeedback assessment and biometric authentication.
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Schmidt, Marcus, Johannes W. Krug, and Georg Rose. "Real-time QRS detection using integrated variance for ECG gated cardiac MRI." Current Directions in Biomedical Engineering 2, no. 1 (September 1, 2016): 255–58. http://dx.doi.org/10.1515/cdbme-2016-0057.
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AbstractDuring magnetic resonance imaging (MRI), a patient’s vital signs are required for different purposes. In cardiac MRI (CMR), an electrocardiogram (ECG) of the patient is required for triggering the image acquisition process. However, a reliable QRS detection of an ECG signal acquired inside an MRI scanner is a challenging task due to the magnetohydrodynamic (MHD) effect which interferes with the ECG. The aim of this work was to develop a reliable QRS detector usable inside the MRI which also fulfills the standards for medical devices (IEC 60601-2-27). Therefore, a novel real-time QRS detector based on integrated variance measurements is presented. The algorithm was trained on ANSI/AAMI EC13 test waveforms and was then applied to two databases with 12-lead ECG signals recorded inside and outside an MRI scanner. Reliable results for both databases were achieved for the ECG signals recorded inside (DBMRI: sensitivity Se = 99.94%, positive predictive value +P = 99.84%) and outside (DBInCarT: Se = 99.29%, +P = 99.72%) the MRI. Due to the accurate R-peak detection in real-time this can be used for monitoring and triggering in MRI exams.
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Jeong, Yeon-Hee, Seung-Hwan Kim, and Young-Mo Yang. "Development of Tight-Fitting Garments with a Portable ECG Monitor to Measure Vital Signs." Journal of the Korean Society of Clothing and Textiles 34, no. 1 (January 31, 2010): 112–25. http://dx.doi.org/10.5850/jksct.2010.34.1.112.
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Verma, Saurav, and Prof Dharmesh. "Low-Cost ECG Analyzing System." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 626–37. http://dx.doi.org/10.22214/ijraset.2022.46695.
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As we are aware of the fact that a fully functional and a healthy heart is a key for a living being to remain alive. In order to monitor the proper functioning of the heart the primitive method of Electrocardiogram (ECG) can be adopted, which keeps the beats in check. The state of having an irregular heartbeat is known as Arrhythmia. The types of Arrhythmia can be apparent based on the factors that cause it. ECG signals consist of ‘PQRST’ waves. To identify the types of Arrhythmia it is vital to analyse the PQRST wave. In order to avoid fatality and to provide immediate medical assistance the ECG of the patient should be analyzed in real time. The project makes use of the AD8232 sensor alongside its interfacing with Arduino Nano to detect ECG signals. Arduino in this case is not only used as an Analog to Digital Converter (ADC) but also as a Sampler. Using Java APIs and Windowing Algorithm the intervals of the PQRST wave is thoroughly analyzed using Novel windowing algorithm. To conduct further analysis and detect the abnormalities in the captured waves it is compared with the standard ECG signals of a healthy person. The final results are shown on the system as well as on the cloud interface to improve the QoS in the healthcare system. The paper thus aims at reducing the overall treatment cost by introducing a low cost ECG Analysis System.
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Goh, Voon Hueh, Muhammad Akmal Ayob, Nurul Izzati Darul Zaman, and Yuan Wen Hau. "Mobile Electrocardiogram Monitoring System with Cloud-Based Approach." Journal of Human Centered Technology 1, no. 2 (August 6, 2022): 105–15. http://dx.doi.org/10.11113/humentech.v1n2.30.
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Cardiovascular disease (CVD) is a heart related disease and is the top silent killer in worldwide. Frequent electrocardiogram (ECG) monitoring for patients with CVD is vital to check if arrhythmia occurs. Though there are lots of commercialized portable ECG monitoring device available, many of them are dedicated to professional clinical staff with complex user interface, or only targeted to specific arrhythmia for home monitoring with lack of data management system. In this study, an in-house developed ECG monitoring device was integrated with an Android-based mobile application through Bluetooth wireless communication and implements Google Cloud Technology. The Android-based mobile app supports main features of real-time ECG graph display, automated location detector, data management system and simple arrhythmia detection. The ECG graph displays acquired ECG signal in real-time by in-house ECG acquisition unit through Bluetooth wireless communication and stored as text files in phone’s local memory. Firebase Authentication and Firebase Storage based on Google Cloud technology are implemented for data management system development. This allows users and administrators to upload or access data securely through online Google Cloud Platform. Three types of heart rhythm, namely normal sinus rhythm, bradycardia and tachycardia can be classified based on heart rate analysis. Smartphone’s location service is enabled to allow user shares their location with caretaker in emergency case. The final solution utilizes the Internet-of-Things (IoT) technology to facilitate heart disease management towards telemedicine applications.
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Goh, Voon Hueh, Muhammad Akmal Ayob, Nurul Izzati Darul Zaman, and Yuan Wen Hau. "Mobile Electrocardiogram Monitoring System with Cloud-Based Approach." Journal of Human Centered Technology 1, no. 2 (August 6, 2022): 105–15. http://dx.doi.org/10.11113/humentech.v1n2.30.
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Cardiovascular disease (CVD) is a heart related disease and is the top silent killer in worldwide. Frequent electrocardiogram (ECG) monitoring for patients with CVD is vital to check if arrhythmia occurs. Though there are lots of commercialized portable ECG monitoring device available, many of them are dedicated to professional clinical staff with complex user interface, or only targeted to specific arrhythmia for home monitoring with lack of data management system. In this study, an in-house developed ECG monitoring device was integrated with an Android-based mobile application through Bluetooth wireless communication and implements Google Cloud Technology. The Android-based mobile app supports main features of real-time ECG graph display, automated location detector, data management system and simple arrhythmia detection. The ECG graph displays acquired ECG signal in real-time by in-house ECG acquisition unit through Bluetooth wireless communication and stored as text files in phone’s local memory. Firebase Authentication and Firebase Storage based on Google Cloud technology are implemented for data management system development. This allows users and administrators to upload or access data securely through online Google Cloud Platform. Three types of heart rhythm, namely normal sinus rhythm, bradycardia and tachycardia can be classified based on heart rate analysis. Smartphone’s location service is enabled to allow user shares their location with caretaker in emergency case. The final solution utilizes the Internet-of-Things (IoT) technology to facilitate heart disease management towards telemedicine applications.
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Golande, Avinash L., and T. Pavankumar. "Automatic Heart Disease Classification Using Ensemble Features Extraction Mechanism from ECG Signals." Webology 18, no. 2 (December 23, 2021): 790–805. http://dx.doi.org/10.14704/web/v18i2/web18354.
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The heart disease detection and classification using the cost-effective tool electrocardiogram (ECG) becomes interesting research considering smart healthcare applications. Automation, accuracy, and robustness are vital demands for an ECG-based heart disease prediction system. Deep learning brings automation to the applications like Computer-Aided Diagnosis (CAD) systems with accuracy improvement compromising robustness. We propose the novel ECG-based heart disease prediction system using the hybrid mechanism to satisfy the automation, accuracy, and robustness requirements. We design the model via the steps of pre-processing, hybrid features formation, and classification. The ECG pre-processing is aiming at suppressing the baseline and powerline interference without loss of heartbeats. We propose a hybrid mechanism that consists of handcrafted and automatic Convolutional Neural Network (CNN) lightweight features for efficient classification. The hybrid feature vector is fed to the deep learning classifier Long Term Short Memory (LSTM) sequentially to predict the disease. The simulation results show that the proposed model reduces the diagnosis errors and time compare to state-of-art methods.
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Rabbani, K. Siddique-e., A. Raihan Abir, and AKM Bodiuzzaman. "Design and Development of a Low Cost Personal Computer Based ECG Monitor." Bangladesh Journal of Medical Physics 4, no. 1 (April 22, 2013): 115–25. http://dx.doi.org/10.3329/bjmp.v4i1.14701.
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ECG equipment is vital for diagnosis of cardiac problems. However, such equipment come from the rich Western countries at a huge cost in both procurement and maintenance, and therefore cannot offer services to a large population in the Third World countries. The only solution is to design and develop such equipment in individual countries by developing local expertise. With three decades of experience, the Dhaka University group has taken a step towards developing prototypes of ECG equipment for dissemination to the healthcare service providers. This paper presents the detailed design of an PC based ECG equipment where optimized choice of components and of the design have been made keeping the cost and maintenance in view, but not sacrificing the quality, and incorporating necessary safety features to protect the patient from known hazards. Both the hardware and the software have been developed locally and are detailed in this paper. Outputs obtained from human subjects are shown which are of reasonable good quality, and have been verified using standard ECG equipment. The PC based ECG system will allow digital post processing of signals for improved diagnosis through software. Besides, this can also become part of a nationwide telemedicine system. DOI: http://dx.doi.org/10.3329/bjmp.v4i1.14701 Bangladesh Journal of Medical Physics Vol.4 No.1 2011 115-125
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Bae, Tae Wuk, Sang Hag Lee, and Kee Koo Kwon. "An Adaptive Median Filter Based on Sampling Rate for R-Peak Detection and Major-Arrhythmia Analysis." Sensors 20, no. 21 (October 29, 2020): 6144. http://dx.doi.org/10.3390/s20216144.
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With the advancement of the Internet of Medical Things technology, many vital sign-sensing devices are being developed. Among the diverse healthcare devices, portable electrocardiogram (ECG) measuring devices are being developed most actively with the recent development of sensor technology. These ECG measuring devices use different sampling rates according to the hardware conditions, which is the first variable to consider in the development of ECG analysis technology. Herein, we propose an R-point detection method using an adaptive median filter based on the sampling rate and analyze major arrhythmias using the signal characteristics. First, the sliding window and median filter size are determined according to the set sampling rate, and a wider median filter is applied to the QRS section with high variance within the sliding window. Then, the R point is detected by subtracting the filtered signal from the original signal. Methods for detecting major arrhythmias using the detected R point are proposed. Different types of ECG signals were used for a simulation, including ECG signals from the MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database, signals generated by a simulator, and actual measured signals with different sampling rates. The experimental results indicated the effectiveness of the proposed R-point detection method and arrhythmia analysis technique.
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Kumpusch, H., D. Hayn, M. Leitner, D. Scherr, F. M. Fruhwald, G. Schreier, and J. Morak. "Near Field Communication-based telemonitoring with integrated ECG recordings." Applied Clinical Informatics 02, no. 04 (2011): 481–98. http://dx.doi.org/10.4338/aci-2010-12-ra-0078.
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SummaryObjective: Telemonitoring of vital signs is an established option in treatment of patients with chronic heart failure (CHF). In order to allow for early detection of atrial fibrillation (AF) which is highly prevalent in the CHF population telemonitoring programs should include electrocardiogram (ECG) signals. It was therefore the aim to extend our current home monitoring system based on mobile phones and Near Field Communication technology (NFC) to enable patients acquiring their ECG signals autonomously in an easy-to-use way.Methods: We prototypically developed a sensing device for the concurrent acquisition of blood pressure and ECG signals. The design of the device equipped with NFC technology and Bluetooth allowed for intuitive interaction with a mobile phone based patient terminal. This ECG monitoring system was evaluated in the course of a clinical pilot trial to assess the system’s technical feasibility, usability and patient’s adherence to twice daily usage.Results: 21 patients (4f, 54 ± 14 years) suffering from CHF were included in the study and were asked to transmit two ECG recordings per day via the telemonitoring system autonomously over a monitoring period of seven days. One patient dropped out from the study. 211 data sets were transmitted over a cumulative monitoring period of 140 days (overall adherence rate 82.2%). 55% and 8% of the transmitted ECG signals were sufficient for ventricular and atrial rhythm assessment, respectively.Conclusion: Although ECG signal quality has to be improved for better AF detection the developed communication design of joining Bluetooth and NFC technology in our telemonitoring system allows for ambulatory ECG acquisition with high adherence rates and system usability in heart failure patients.
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Hu, Bowen, Jian Zhao, Xin Liang, Changzhen Ren, Na Li, and Chun Liang. "A case of complete atrioventricular block associated with primary cardiac lymphoma reversed without cardiac pacemaker implantation." Journal of International Medical Research 50, no. 4 (April 2022): 030006052210897. http://dx.doi.org/10.1177/03000605221089780.
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Primary cardiac lymphoma (PCL) is a rare malignant lymphoma that is characteristically confined to the heart and/or pericardium. Here, the case of a 70-year-old male patient with complete atrioventricular block (AVB) associated with PCL is presented. The patient had a 10-month history of palpitation and electrocardiogram (ECG) showed a complete AVB. Additionally, transthoracic echocardiography indicated pericardial effusion where atypical lymphoid cells were identified by pericardiocentesis. Subsequent mediastinal lymph node biopsy revealed non-germinal centre diffuse large B-cell lymphoma. Therefore, a diagnosis of PCL was confirmed. As the patient’s vital signs were stable, he was prescribed chemotherapy without pacemaker implantation. After chemotherapy, the patient achieved remission and dynamic ECG demonstrated no recurrence of AVB. The present case demonstrates that although PCL initially manifesting as complete AVB is rare, this possibility should not be ignored when a new AVB without definite aetiology is encountered. In addition, if the vital signs of the patient are stable, pacemaker implantation may be postponed until the treatment effect of chemotherapy has been assessed.
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Oliveira, Regina G., Pedro M. M. Correia, Ana L. M. Silva, Pedro M. C. C. Encarnação, Fabiana M. Ribeiro, Ismael F. Castro, and João F. C. A. Veloso. "Development of a New Integrated System for Vital Sign Monitoring in Small Animals." Sensors 22, no. 11 (June 3, 2022): 4264. http://dx.doi.org/10.3390/s22114264.
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Monitoring the vital signs of mice is an essential practice during imaging procedures to avoid populational losses and improve image quality. For this purpose, a system based on a set of devices (piezoelectric sensor, optical module and thermistor) able to detect the heart rate, respiratory rate, body temperature and arterial blood oxygen saturation (SpO2) in mice anesthetized with sevoflurane was implemented. Results were validated by comparison with the reported literature on similar anesthetics. A new non-invasive electrocardiogram (ECG) module was developed, and its first results reflect the viability of its integration in the system. The sensors were strategically positioned on mice, and the signals were acquired through a custom-made printed circuit board during imaging procedures with a micro-PET (Positron Emission Tomography). For sevoflurane concentration of 1.5%, the average values obtained were: 388 bpm (beats/minute), 124 rpm (respirations/minute) and 88.9% for the heart rate, respiratory rate and SpO2, respectively. From the ECG information, the value obtained for the heart rate was around 352 bpm for injectable anesthesia. The results compare favorably to the ones established in the literature, proving the reliability of the proposed system. The ECG measurements show its potential for mice heart monitoring during imaging acquisitions and thus for integration into the developed system.
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Xiao, Xueliang, Ke Dong, Chenhao Li, Guanzheng Wu, Hongtao Zhou, and Yanjia Gu. "A comfortability and signal quality study of conductive weave electrodes in long-term collection of human electrocardiographs." Textile Research Journal 89, no. 11 (July 13, 2018): 2098–112. http://dx.doi.org/10.1177/0040517518786275.
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Long-term electrocardiogram (ECG) recording can reveal some vital cardiovascular disorders and provide warning of human sudden cerebral or vascular diseases in advance. This requires high-quality ECG skin electrodes. Gel (Ag/AgCl) electrodes were reported to have good signal quality in ECG acquisition, but easily caused human skin irritation or allergy. Consequently, textile electrodes have attracted more attention for long-term ECG acquisition. In this paper, eight woven fabrics with diverse yarns and weft densities were fabricated in plain and honeycomb structures. The fabrics were investigated in terms of comfortability, fabric–skin contact impedance and acquired bio-signal quality. Honeycomb weave electrodes were measured with a high comfort level from subjective and objective views, including pleasant tactile comfort, high visual acceptance, good air permeability and good heat transfer. Weave electrodes made of all conductive filaments in high density had low skin contact impedance and high-quality ECG signals. An increase of compression load on weave electrodes resulted in a decrease of contact impedance with a high signal quality. A conductive honeycomb weave with unit repeat of 6*6 warps*wefts presented the highest score of acquired ECG signals of all studied electrodes based on the qualities of the QRS complex, P and T waves, R peak amplitude and variation and signal-to-noise ratio. This study contributes to the future design and fabrication of textile electrodes using honeycomb weave in long-term and real-time collection of human ECGs.
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Jamil, Sonain, and MuhibUr Rahman. "A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia." Journal of Imaging 8, no. 3 (March 10, 2022): 70. http://dx.doi.org/10.3390/jimaging8030070.
Full textAbstract:
Cardiovascular diseases (CVDs) are the primary cause of death. Every year, many people die due to heart attacks. The electrocardiogram (ECG) signal plays a vital role in diagnosing CVDs. ECG signals provide us with information about the heartbeat. ECGs can detect cardiac arrhythmia. In this article, a novel deep-learning-based approach is proposed to classify ECG signals as normal and into sixteen arrhythmia classes. The ECG signal is preprocessed and converted into a 2D signal using continuous wavelet transform (CWT). The time–frequency domain representation of the CWT is given to the deep convolutional neural network (D-CNN) with an attention block to extract the spatial features vector (SFV). The attention block is proposed to capture global features. For dimensionality reduction in SFV, a novel clump of features (CoF) framework is proposed. The k-fold cross-validation is applied to obtain the reduced feature vector (RFV), and the RFV is given to the classifier to classify the arrhythmia class. The proposed framework achieves 99.84% accuracy with 100% sensitivity and 99.6% specificity. The proposed algorithm outperforms the state-of-the-art accuracy, F1-score, and sensitivity techniques.
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Vincent, Rony. "From a laboratory to the wearables: a review on history and evolution of electrocardiogram." Iberoamerican Journal of Medicine 4, no. 4 (September 6, 2022): 248–55. http://dx.doi.org/10.53986/ibjm.2022.0038.
Full textAbstract:
The development of electrocardiography, one of the top scientific breakthroughs of the 20th century, occurred in the field of cardiology. The history of the ECG began long before its invention, with the advent of the study of electricity in the medical field. The idea of electrophysiology and Waller's initial recording of the ‘electrogram’ encouraged Willem Einthoven to develop new string galvanometers and turn this remarkable physiologic occurrence into a vital clinical recording tool. It has progressed from Einthoven's innovation to wearable technology. In the first part of the 20th century, a number of inventive people achieved a remarkable succession of discoveries and advancements that led to the development of the 12-lead ECG as we know it today. It went further than that. The evolution of science and technology over the years has allowed for continual development in terms of usefulness, ranging from five operators to one operator meant to record the ECG trace, and mobility, ranging from around 300 Kg to roughly around 1 Kg. Electrocardiographs in minimized form now exist thanks to the modern era of digitalization. We will go over the significant processes in the development of the ECG in this article.
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Tahat, Ashraf A. "Mobile Messaging Services-Based Personal Electrocardiogram Monitoring System." International Journal of Telemedicine and Applications 2009 (2009): 1–7. http://dx.doi.org/10.1155/2009/859232.
Full textAbstract:
A mobile monitoring system utilizing Bluetooth and mobile messaging services (MMS/SMSs) with low-cost hardware equipment is proposed. A proof of concept prototype has been developed and implemented to enable transmission of an Electrocardiogram (ECG) signal and body temperature of a patient, which can be expanded to include other vital signs. Communication between a mobile smart-phone and the ECG and temperature acquisition apparatus is implemented using the popular personal area network standard specification Bluetooth. When utilizing MMS for transmission, the mobile phone plots the received ECG signal and displays the temperature using special application software running on the client mobile phone itself, where the plot can be captured and saved as an image before transmission. Alternatively, SMS can be selected as a transmission means, where in this scenario, dedicated application software is required at the receiving device. The experimental setup can be operated for monitoring from anywhere in the globe covered by a cellular network that offers data services.
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Rahman, Atta-ur, Rizwana Naz Asif, Kiran Sultan, Suleiman Ali Alsaif, Sagheer Abbas, Muhammad Adnan Khan, and Amir Mosavi. "ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches." Computational Intelligence and Neuroscience 2022 (July 31, 2022): 1–12. http://dx.doi.org/10.1155/2022/6852845.
Full textAbstract:
According to the World Health Organization (WHO) report, heart disease is spreading throughout the world very rapidly and the situation is becoming alarming in people aged 40 or above (Xu, 2020). Different methods and procedures are adopted to detect and diagnose heart abnormalities. Data scientists are working on finding the different methods with the required accuracy (Strodthoff et al., 2021). Electrocardiogram (ECG) is the procedure to find the heart condition in the waveform. For ages, the machine learning techniques, which are feature based, played a vital role in the medical sciences and centralized the data in cloud computing and having access throughout the world. Furthermore, deep learning or transfer learning widens the vision and introduces different transfer learning methods to ensure accuracy and time management to detect the ECG in a better way in comparison to the previous and machine learning methods. Hence, it is said that transfer learning has turned world research into more appropriate and innovative research. Here, the proposed comparison and accuracy analysis of different transfer learning methods by using ECG classification for detecting ECG Arrhythmia (CAA-TL). The CAA-TL model has the multiclassification of the ECG dataset, which has been taken from Kaggle. Some of the healthy and unhealthy datasets have been taken in real-time, augmented, and fused with the Kaggle dataset, i.e., Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH dataset). The CAA-TL worked on the accuracy of heart problem detection by using different methods like ResNet50, AlexNet, and SqueezeNet. All three deep learning methods showed remarkable accuracy, which is improved from the previous research. The comparison of different deep learning approaches with respect to layers widens the research and gives the more clarity and accuracy and at the same time finds it time-consuming while working with multiclassification with massive dataset of ECG. The implementation of the proposed method showed an accuracy of 98.8%, 90.08%, and 91% for AlexNet, SqueezeNet, and ResNet50, respectively.