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Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG).
Sensors (Basel)
March 2024
Department of Electronics, Faculty of Electronic Engineering and Technologies, Technical University of Sofia, 8 Kliment Ohridski Blvd., 1000 Sofia, Bulgaria.
Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focused on sonified ECG modality, which can be transmitted as an acoustic wave and received by GSM (Global System for Mobile Communications) microphones. Thus, the wireless connection between the patient module and the cloud server can be provided over an audio channel, such as a standard telephone call or audio message.
View Article and Find Full Text PDFHybrid Deep Learning and Discrete Wavelet Transform-Based ECG Biometric Recognition for Arrhythmic Patients and Healthy Controls.
Sensors (Basel)
May 2023
Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan.
The intrinsic and liveness detection behavior of electrocardiogram (ECG) signals has made it an emerging biometric modality for the researcher with several applications including forensic, surveillance and security. The main challenge is the low recognition performance with datasets of large populations, including healthy and heart-disease patients, with a short interval of an ECG signal. This research proposes a novel method with the feature-level fusion of the discrete wavelet transform and a one-dimensional convolutional recurrent neural network (1D-CRNN).
View Article and Find Full Text PDFA 746 nW ECG Processor ASIC Based on Ternary Neural Network.
IEEE Trans Biomed Circuits Syst
August 2022
This paper presents an ultra-low power electrocardiography (ECG) processor application-specific integrated circuit (ASIC) for the real-time detection of abnormal cardiac rhythms (ACRs). The proposed ECG processor can support wearable or implantable ECG devices for long-term health monitoring. It adopts a derivative-based patient adaptive threshold approach to detect the R peaks in the PQRST complex of ECG signals.
View Article and Find Full Text PDFA 2.66 µW Clinician-Like Cardiac Arrhythmia Watchdog Based on P-QRS-T for Wearable Applications.
IEEE Trans Biomed Circuits Syst
October 2022
A wearable electrocardiogram (ECG) device is an effective tool for managing cardiovascular diseases. This paper presents a low power clinician-like cardiac arrhythmia watchdog (CAW) for wearable ECG devices. The CAW is based on a novel P-QRS-T detection algorithm that makes use of clinical features to identify abnormalities.
View Article and Find Full Text PDFMeasurement of the cardiac time intervals of the fetal ECG utilising a computerised algorithm: A retrospective observational study.
JRSM Cardiovasc Dis
May 2022
Department of Pediatrics, Columbia University Medical Center, Morgan Stanley Children's Hospital, New York, USA.
Objective: Establish whether the reliable measurement of cardiac time intervals of the fetal ECG can be automated and to address whether this approach could be used to investigate large datasets.
Design: Retrospective observational study.
Setting: Teaching hospitals in London UK, Nottingham UK and New York USA.
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