Slope-Based Event-Driven Feature Extraction For Cardiac Arrhythmia Classification - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Slope-Based Event-Driven Feature Extraction For Cardiac Arrhythmia Classification

Résumé

To detect cardiovascular diseases (CVD), electrocardiogram (ECG) of a patient must be recorded and analyzed for a long period. For an effective diagnosis, the ECG recording system must automatically adapt to new patients. This paper presents a low-complexity artificial neural network that exclusively uses the consecutive slopes of ECG signal as inputs. These features are extracted using a level-crossing ADC and a simple TDC-based event-driven processing chain. The proposed clockless system can detect arrhythmias in ECG with 98.4% accuracy and reduce the ANN hardware complexity by more than half compared to recent literature. It is perfectly adapted to integrated wearable monitoring systems and shows good adaptability to new patients. Keywords-Artificial neural network (ANN), electrocardiogram (ECG), cardiac arrhythmia classification (CAC), event-driven, time-to-digital converter (TDC), levelcrossing ADC
Fichier principal
Vignette du fichier
BioCAS 2021 - Julien Duforest - Slope-Based Event-driven Feature Extraction For Cardiac Arrhythmia Classification - submitted.pdf (973.62 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03544633 , version 1 (26-01-2022)

Identifiants

Citer

Julien Duforest, Benoit Larras, Deepu John, Olev Märtens, Antoine Frappé. Slope-Based Event-Driven Feature Extraction For Cardiac Arrhythmia Classification. 2021 IEEE Biomedical Circuits and Systems Conference (BioCAS), Oct 2021, Berlin, Germany. pp.01-04, ⟨10.1109/BioCAS49922.2021.9644957⟩. ⟨hal-03544633⟩
34 Consultations
51 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More