TY - GEN
T1 - Leveraging blockchain for retraining deep learning architecture in patient-specific arrhythmia classification
AU - Juneja, Amit
AU - Marefat, Michael
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/4/6
Y1 - 2018/4/6
N2 - Stacked Denoising Autoencoders (SDA) are deep networks which have gained popularity owing to their superior performance in image classification applications, but they haven't been used much in healthcare applications. SDA can be efficiently retrained to adapt to large streams of data, and this property is used in this work to develop a technique for classification of arrhythmias in a patient-specific manner. This approach is particularly useful in continuous remote systems because they gather large amounts of data for longer periods of time. Blockchain is a decentralized distributed ledger which secures transactions with cryptography. It is proposed as an access control manager to securely store and access data required by the classifier during retraining in real-time from an external data storage. This work uses MIT-BIH Arrhythmia database and the results show an increased accuracy for Ventricular Ectopic Beats (VEB) (99.15%) and Supraventricular Ectopic Beats (SVEB) (98.55%), which is higher than the published results of deep networks that are not retrained.
AB - Stacked Denoising Autoencoders (SDA) are deep networks which have gained popularity owing to their superior performance in image classification applications, but they haven't been used much in healthcare applications. SDA can be efficiently retrained to adapt to large streams of data, and this property is used in this work to develop a technique for classification of arrhythmias in a patient-specific manner. This approach is particularly useful in continuous remote systems because they gather large amounts of data for longer periods of time. Blockchain is a decentralized distributed ledger which secures transactions with cryptography. It is proposed as an access control manager to securely store and access data required by the classifier during retraining in real-time from an external data storage. This work uses MIT-BIH Arrhythmia database and the results show an increased accuracy for Ventricular Ectopic Beats (VEB) (99.15%) and Supraventricular Ectopic Beats (SVEB) (98.55%), which is higher than the published results of deep networks that are not retrained.
KW - Arrhythmia Classification
KW - Blockchain
KW - Patient Specific
KW - Remote Continuous Health Systems
KW - Retraining
KW - Stacked Denoising Autoencoders
UR - http://www.scopus.com/inward/record.url?scp=85050893998&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050893998&partnerID=8YFLogxK
U2 - 10.1109/BHI.2018.8333451
DO - 10.1109/BHI.2018.8333451
M3 - Conference contribution
AN - SCOPUS:85050893998
T3 - 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018
SP - 393
EP - 397
BT - 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018
Y2 - 4 March 2018 through 7 March 2018
ER -