QRS Peaks, P and T Waves Identification in ECG

Costa, R.C.; Winkert, Thaís; Manhães, Aline Gesualdi; Teixeira, João Paulo

doi:10.1016/j.procs.2021.01.252

Cited by 17 publications

(7 citation statements)

References 6 publications

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“…Left bundle branch conduction block, also known as left bundle branch block, results from delayed conduction of electrical excitation in the left bundle branch of the heart or interrupted; the excitation can only be transmitted from the right to the left ventricle of the heart, resulting in some delay in the excitation of the left ventricle. Right bundle branch conduction block is caused by a block in the right bundle branch of the heart, which in turn causes electrical excitation to fail via the pathway into the right ventricle and must be signaled by signals from the left ventricle [ 18 ]. However, the electrical signals from the left ventricle must pass through myocardial afferents, and this pathway is transmitted more slowly than the original Hitchcock–Purkinje fiber pathway, so on the ECG, the patient will have a wider complex wave.…”

Section: Mit-bih Cardiac Database For Online Automatic Diagnosis Of Cardiac Arrhythmiasmentioning

confidence: 99%

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Yan

Zhang

2021

Journal of Healthcare Engineering

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Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on the hidden and sudden nature of the MIT-BIH ECG database signal and the small-signal amplitude, this paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data, to learn the deep-seated essential features of the target data, combine the characteristics of the information processing mechanism of the arrhythmia online automatic diagnosis system, and automatically extract the spatial features and temporal characteristics of the diagnostic data. First, a combination of median filter and bandstop filter is used to preprocess the data in the ECG database with individual differences in ECG waveforms, and there are problems of feature inaccuracy and useful feature omission which cannot effectively extract the features implied behind the massive ECG signals. Its diagnostic algorithm integrates feature extraction and classification into one, which avoids some bias in the feature extraction process and provides a new idea for the automatic diagnosis of cardiovascular diseases. To address the problem of feature importance variability in the temporal data of the MIT-BIH ECG database, a hybrid model is constructed by introducing algorithms in deep neural networks, which can enhance its diagnostic efficiency.

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Section: Mit-bih Cardiac Database For Online Automatic Diagnosis Of Cardiac Arrhythmiasmentioning

confidence: 99%

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Yan

Zhang

2021

Journal of Healthcare Engineering

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“…Finally, P-peak and T-peak are identified. Further alternative approaches to identifying these five peaks can be found in (Costa, et al, 2021) and (Turnip, 2019).…”

Section: The Hamilton Algorithmmentioning

confidence: 99%

Prototype of Portable Heart Monitoring System using BITalino

et al. 2023

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Jantung adalah organ vital yang menuntut perhatian khusus, terutama untuk orang dengan resiko serangan jantung. Bagi orang kategori ini, diperlukan detektor detak jantung yang bekerja secara kontinu dan real-time yang dapat mendeteksi adanya gangguan jantung secara dini. Pada penelitian ini, penulis mengajukan prototipe sistem monitoring jantung portable (PSMJP) dengan menggunakan modul bio-signal BITalino. Hasil pengukuran diproses pada perangkat komputer yang terhubung dengan BITalino melalui transmisi Bluetooth. Suatu program pemroses sinyal dirancang dengan menggunakan Algorithma Hamilton. Tingkat keberhasilan deteksi pada pengujian terhadap sampel EKG mentah dan pengukuran EKG mentah adalah 100%. PSMJP diujikan kepada 15 naracoba untuk kondisi duduk dan kondisi berjalan. PSMJP berfungsi baik pada 29 dari 30 pengukuran, dimana sinyal elektrik dari jantung terbukti dapat diproses dan memberikan hasil akhir berupa fitur-fitur gelombang detak jantung dan laju detak jantung.

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“…Patients can share their data with healthcare practitioners for remote monitoring, receive early warnings for significant changes, and better manage their diseases. In addition to improving patient outcomes, this lessens the strain on healthcare systems by lowering emergency room visits and readmissions to hospitals [10].…”

Section: The Influence Of Constant Monitoringmentioning

confidence: 99%

Implementation of Wearable IoT Devices for Continuous Physiological Monitoring and Analysis

2023

RJCSE

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The development of wearable devices for ongoing physiological monitoring and analysis has been made possible by the emergence of the Internet of Things (IoT), which has completely changed the healthcare industry. This study discusses the use of such wearable IoT devices and their potential to improve healthcare delivery. Our research focuses on the development, manufacture, and implementation of wearable sensors capable of real-time monitoring of vital signs like heart rate, body temperature, blood pressure, and activity levels. These devices leverage the Internet of Things to transmit data wirelessly to a cloud service for further analysis. The core components of these gadgets are miniature sensors, low-power microcontrollers, and wireless communication modules. These developments permit the invisible and continuous collection of data, which aids in the early detection of irregularities and permits urgent medical treatments. Data collected by these devices is processed by sophisticated analytics and machine learning algorithms before being transmitted securely to a cloud-based server. As a result, doctors and nurses are able to anticipate health issues, monitor their patients' physical conditions in real time, and provide individualised care. Los patients have more control over their own medical decisions since they have access to their own data. Frequent use of Internet-connected devices for monitoring and analysing physical phenomena may lead to better medical care, lower hospitalisation costs, and higher quality of life. Additionally, these devices have the potential to revolutionise clinical research by providing extensive real-world data for medical research. These wearable IoT devices have the potential to enhance the management of chronic illnesses, encourage early treatments, and raise general wellbeing. Wearable physiological monitoring technology has a lot of potential for the future of healthcare thanks to the advancements of the internet of things.

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QRS Peaks, P and T Waves Identification in ECG

Cited by 17 publications

References 6 publications

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Prototype of Portable Heart Monitoring System using BITalino

Implementation of Wearable IoT Devices for Continuous Physiological Monitoring and Analysis

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