A VLSI Chip for the Abnormal Heart Beat Detection Using Convolutional Neural Network

Chen, Yuan-Ho; Chen, Szi-Wen; Chang, Pei-Jung; Hua, Hsin-Tung; Lin, Shinn‐Yn; Chen, Rou‐Shayn

doi:10.3390/s22030796

Cited by 10 publications

(2 citation statements)

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“…The suggested approach helps to provide high accuracy to a diagnostic fault in an acceptable amount of time. The work in [46], implements an abnormal heartbeat detector using DL, also a complementary metal-oxide-semiconductor (CMOS) design is presented to produce a wearable device. Raw ECG signals are fed into the CNN classifier and very large-scale integration (VLSI) chip is implemented accordingly.…”

Section: Related Workmentioning

confidence: 99%

General and patient-specific seizure classification using deep neural networks

Massoud

Abdelzaher

Kuhlmann

et al. 2023

Analog Integr Circ Sig Process

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Seizure prediction algorithms have been central in the field of data analysis for the improvement of epileptic patients’ lives. The most recent advancements of which include the use of deep neural networks to present an optimized, accurate seizure prediction system. This work puts forth deep learning methods to automate the process of epileptic seizure detection with electroencephalogram (EEG) signals as input; both a patient-specific and general approach are followed. EEG signals are time structure series motivating the use of sequence algorithms such as temporal convolutional neural networks (TCNNs), and long short-term memory networks. We then compare this methodology to other prior pre-implemented structures, including our previous work for seizure prediction using machine learning approaches support vector machine and random under-sampling boost. Moreover, patient-specific and general seizure prediction approaches are used to evaluate the performance of the best algorithms. Area under curve (AUC) is used to select the best performing algorithm to account for the imbalanced dataset. The presented TCNN model showed the best patient-specific results than that of the general approach with, AUC of 0.73, while ML model had the best results for general classification with AUC of 0.75.

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Section: Related Workmentioning

confidence: 99%

General and patient-specific seizure classification using deep neural networks

Massoud

Abdelzaher

Kuhlmann

et al. 2023

Analog Integr Circ Sig Process

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“…The lectrocardiogram, which detects electrophysiological signals from the heart muscle, is considered a favourable monitoring method [1]. It is used to assist in the diagnosis of cardiac diseases and plays an important role in determining the effects of drugs or electrolyte conditions on the heart and the status of artificial heart pacing [2].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based ECG-Assisted Diagnosis for Coal Workers

Wang

Chen²,

Tian

et al. 2022

IJERPH

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Objective: To process and extract electrocardiogram (ECG, ECG, or EKG) features using a convolutional neural network (CNN) to establish an ECG-assisted diagnosis model. Methods: Coal workers who underwent physical examinations at Gequan Mine Hospital and Dongpang Mine Hospital of Hebei Jizhong Energy from July 2020 to September 2020 were selected as the study subjects. The ECG images were preprocessed. We use Python software and convolutional neural network to establish ECG images recognition and classification model.We usecalibration curve, calibration-in-the-large, Brier score, specificity, sensitivity, F1 score, Kappa value, accuracy, and area under the curve (AUC) of ROC to evaluate the performance of the model. Results: The number of abnormal ECG results was 849, and the rate of abnormal results was 25.02%. The test set accuracies of the sinus bradycardia model, nonspecific intraventricular conduction delay model, myocardial ischemia model, and sinus tachycardia model were 97.66%, 96.49%, 93.62%, and 93.02%, respectively; sensitivities were 96.63%, 96.30%, 96.88% and 95.24%, respectively; specificities were 98.78%, 96.67%, 86.67%, and 90.90%, respectively; Brier scores were 0.03, 0.07, 0.09, and 0.11, respectively; Calibration-in-the-large values were 0.026, 0.110, 0.041, and 0.098, respectively. Conclusions: The convolutional neural network model can accurately identify the main ECG abnormality types of coal workers. Additionally, the main ECG abnormalities in these coal company workers were sinus bradycardia, non-specific intraventricular conduction delay, myocardial ischemia, and sinus tachycardia.

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