Evaluation of Level-Crossing ADCs for ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Evaluation of Level-Crossing ADCs for Event-Driven ECG Classification
Author(s) :
Saeed, Maryam [Auteur]
University College Dublin [Dublin] [UCD]
Wang, Qingyuan [Auteur]
University College Dublin [Dublin] [UCD]
Martens, Olev [Auteur]
Tallinn University of Technology [TTÜ]
Larras, Benoit [Auteur]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Frappe, Antoine [Auteur]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Cardiff, Barry [Auteur]
University College Dublin [Dublin] [UCD]
Deepu, John [Auteur]
University College Dublin [Dublin] [UCD]
University College Dublin [Dublin] [UCD]
Wang, Qingyuan [Auteur]
University College Dublin [Dublin] [UCD]
Martens, Olev [Auteur]
Tallinn University of Technology [TTÜ]
Larras, Benoit [Auteur]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Frappe, Antoine [Auteur]
Microélectronique Silicium - IEMN [MICROELEC SI - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Cardiff, Barry [Auteur]
University College Dublin [Dublin] [UCD]
Deepu, John [Auteur]
University College Dublin [Dublin] [UCD]
Journal title :
IEEE Transactions on Biomedical Circuits and Systems
Pages :
pp 1129-1139
Publisher :
Institute of Electrical and Electronics Engineers
Publication date :
2021-12
ISSN :
1932-4545
HAL domain(s) :
Sciences de l'ingénieur [physics]
English abstract : [en]
In this paper, a new methodology for choosing design parameters of level-crossing analog-to-digital converters (LC-ADCs) is presented that improves sampling accuracy and reduces the data stream rate. Using the MIT-BIH ...
Show more >In this paper, a new methodology for choosing design parameters of level-crossing analog-to-digital converters (LC-ADCs) is presented that improves sampling accuracy and reduces the data stream rate. Using the MIT-BIH Arrhythmia dataset, several LC-ADC models are designed, simulated and then evaluated in terms of compression and signal-to-distortion ratio. A new one-dimensional convolutional neural network (1D-CNN) based classifier is presented. The 1D-CNN is used to evaluate the event-driven data from several LC-ADC models. With uniformly sampled data, the 1D-CNN has 99.49%, 92.4% and 94.78% overall accuracy, sensitivity and specificity, respectively. In comparison, a 7-bit LC-ADC with 2385Hz clock frequency and 6-bit clock resolution offers 99.2%, 89.98% and 91.64% overall accuracy, sensitivity and specificity, respectively. It also offers 3x data compression while maintaining a signal-to-distortion ratio of 21.19dB. Furthermore, it only requires 49% floating-point operations per second (FLOPS) for cardiac arrhythmia classification in comparison with the uniformly sampled ADC. Finally, an open-source event-driven arrhythmia database is presented.Show less >
Show more >In this paper, a new methodology for choosing design parameters of level-crossing analog-to-digital converters (LC-ADCs) is presented that improves sampling accuracy and reduces the data stream rate. Using the MIT-BIH Arrhythmia dataset, several LC-ADC models are designed, simulated and then evaluated in terms of compression and signal-to-distortion ratio. A new one-dimensional convolutional neural network (1D-CNN) based classifier is presented. The 1D-CNN is used to evaluate the event-driven data from several LC-ADC models. With uniformly sampled data, the 1D-CNN has 99.49%, 92.4% and 94.78% overall accuracy, sensitivity and specificity, respectively. In comparison, a 7-bit LC-ADC with 2385Hz clock frequency and 6-bit clock resolution offers 99.2%, 89.98% and 91.64% overall accuracy, sensitivity and specificity, respectively. It also offers 3x data compression while maintaining a signal-to-distortion ratio of 21.19dB. Furthermore, it only requires 49% floating-point operations per second (FLOPS) for cardiac arrhythmia classification in comparison with the uniformly sampled ADC. Finally, an open-source event-driven arrhythmia database is presented.Show less >
Language :
Anglais
Popular science :
Non
Source :
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