ECG diagnostic support system (EDSS): A deep learning neural network based classification system for detecting ECG abnormal rhythms from a low-powered wearable biosensors

Panganiban, Edward B.; Paglinawan, Arnold C.; Chung, Wen Yaw; Paa, Gilbert Lance S.

doi:10.1016/j.sbsr.2021.100398

Cited by 30 publications

(19 citation statements)

References 38 publications

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“…As a result of single channel measurements, it is observed that ECG signals can be measured successfully, heartbeats can be detected, and the obtained ECG signals are compatible with those given in the literature. Results clearly show that the obtained signals can be used in HCI systems (Kashou et al, 2020;Panganiban et al, 2021).…”

Section: Measurement and Evaluation Of Ecg Signalsmentioning

confidence: 82%

Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications

ÖNER¹,

Gürkan

KARAPINAR³

et al. 2022

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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A multi-channel measurement system used to measure electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG) and electrooculogram (EOG) biosignals has been designed and prototyped. The designed system has 16 configurable measurement channels. Of the 16 channels the developed system has, 8 have been designed for EEG, 2 for EMG, 2 for EOG, 1 for ECG measurements, the remaining 3 have been reserved as backup channels. In circuit design, biosignal amplifier design principles have been applied by taking into account the characteristics of the biosignal to be measured for each channel, such as bandwidth, frequency, amplitude, noise level. Modules such as instrumentation amplifier, filter, DC suppression unit, amplifier, DC level determination unit, analogdigital converter, optical isolation unit, power supply have been designed to perform biosignal measurements through these channels. Biosignals measured by the developed system can be shifted to the desired threshold level with the help of the analog output reference voltage, converted to digital data 10-bit resolution and transferred to the computer environment in real time. The data transferred to the computer can be used in C#, Excel, MATLAB, and LabVIEW platforms. The novelty of the developed system is that any of the four desired biosignal types can be measured from any channel. In addition, another feature of the system is that it can work with real-time data without being dependent on the databases serving for human-computer interface applications. In experimental studies with some researchers for the performance tests of the system, ECG, EEG, EMG and EOG signals have been recorded with different module configurations, and signal processing stages were carried out to be used for human-computer applications.

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Section: Measurement and Evaluation Of Ecg Signalsmentioning

confidence: 82%

Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications

ÖNER¹,

Gürkan

KARAPINAR³

et al. 2022

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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“…This model outperformed three other combination types from continuous wavelet transform (CWT) and ANN. In addition, an ECG diagnostic support system (EDSS) [ 84 ] was developed to detect ECG arrhythmia utilising deep 2D-CNN with images based on spectrograms. The advantages of using spectrogram images as input are that the visual examination such as identification of R-peak or P-peak is not needed, and is reliable.…”

Section: Resultsmentioning

confidence: 99%

Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review

Huang

Wang

Ramsey

et al. 2022

Sensors

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Cardiovascular disease (CVD) is the world’s leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.

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“…In 2021, Panganiban et al [10] have introduced the classification process for ECG arrhythmia that utilizes CNN with images based on spectrograms with no ECG visual examination like P-peak or R-peak detection. Additionally, the proposed CNN model disregarded the noise parameters as the ECG data converted to 2D images.…”

Section: Related Workmentioning

confidence: 99%

Heart Disease Classification Using Multiple K-PCA and Hybrid Deep Learning Approach

Kusuma¹,

R²

2022

Computer Systems Science and Engineering

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ECG diagnostic support system (EDSS): A deep learning neural network based classification system for detecting ECG abnormal rhythms from a low-powered wearable biosensors

Cited by 30 publications

References 38 publications

Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications

Development of a Multichannel Bioinstrumentation System for Human-Computer Interface Applications

Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review

Heart Disease Classification Using Multiple K-PCA and Hybrid Deep Learning Approach

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