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EED-Newsletter-Vol-2-Issue-1-2017

PAGE 26 Editorials from

PAGE 26 Editorials from EE Faculty Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias Prof. Serkan Kiranyaz Professor at the EED Qatar University “If people have no history of heart problems, we can only analyse their normal heart rhythm. How can we know what their abnormal rhythm would look like? Our research found that it is possible to apply machine learning methods to model all the potential arrhythmic heartbeats of a healthy person,” “Our system is personalized for each user. A healthy user’s heart rate data and the synthesized abnormal heartbeats are entered into the system to serve as a baseline. This way the system is trained to monitor the user's heart rate and identify irregular heartbeats as soon as they occur,” “…This approach not only achieved an average detection probability higher than 99.4% with a very low false-alarm rate. The system can be embedded in a lowcost portable device for real-time heart monitoring.” Each year more than 7 million people die from cardiac arrhythmias. Even with the electrocardiogram (ECG) acquisition technology that we reach today, no robust solution yet exists to detect such heart anomalies right at the moment they occur. It is partially because everybody’s ECG is unique and one’s normal heart beats may look like another’s arrhythmic beats or vice versa. Plus anybody’s ECG can show significant variations in time with respect to the variations of the physical condition (stress, excess caffeine, drugs, smoking, etc.), age, environment (e.g., at high altitude, underwater, deep sleep), etc. The worst of all, the arrhythmic ECG beats may have sometimes significant and sometimes insignificant differences from the normal beats (e.g. see Fig.1). This is why only the expertise and the trained eye of a Cardiologists can detect the arrhythmic beats accurately. Otherwise for a “healthy person” with no past history of cardiac arrhythmia, automatic detection of the first-time occurrence of arrhythmic beat(s) poses the ultimate challenge that has never been addressed up to date. I was a part of the team of researchers along with Professor Moncef Gabbouj from TUT, Finland and Professor Turker Ince from IUE, Turkey. The team analysed extensive heart rate and arrhythmia datasets collected during their earlier ECG research. We succeeded to “mimic” expert Cardiologists’ ability to detect early arrhythmia. First, they managed to artificially model the heart degradation in the signal domain to synthesize the potential arrhythmic beats of a healthy person. Then using their recent Machine Learning approach that achieved the current state-ofthe-art solution for patientspecific ECG classification, they managed to create a personalized heart monitoring system for that person. This approach not only achieved an average detection probability higher than 99.4% with a very low false-alarm rate. The system can be embedded in a low-cost portable device for real-time heart monitoring. Fig. 1: Normal (N) vs. Abnormal (S and V) beats from different subjects in MIT-BIH dataset. The proposed system illustrated in Fig. 2, was awraded1 st and 2 nd places at the International Physionet Challenge 2017 and 2016, respectively, on ECG and PCG anomaly detection. EED NEWSLETTER VOL. 2, ISSUE 1

VOLUME 1, ISSUE 1 PAGE 27 Fig. 2: The system overflow of the proposed solution in an illustrative Client/Server application. The findings appeared recently in Scientific Reports – Naturehttps:// www.nature.com/srep/ S. Kiranyaz, T. Ince, and M. Gabbouj, "Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias," Scientific Reports - Nature, 2017. See:https://www.nature.com/article s/s41598-017-09544-z Recent Achievements in Cardiac Signal Processing October 2017: My research team won the 1 st place in Physionet Challenge 2017 (a.k.a. Alive- Cor) among 75 teams. Physionet is the grand forum of “Computing in Cardiology” which offers free access via the web to large physiologic signals and related open-source software. September 2016: Our proposal ranked the 2nd place in PhysioNet Challenge 2016 among 48 teams. May 2016: Our article, “Real-Time Patient-Specific ECG Classification by 1D Convolutional Neural Networks” became the 5 th most-popular and the 4 th most-cited paperin IEEE Transaction on Biomedical Engineering. 2010-2015: The article, “Evolutionary Artificial Neural Networks by Multi- Dimensional Particle Swarm Optimization“, is the 4 th most cited paper in the Neural Networks journal. Ongoing Research Projects Patient-specific and Real-Time Heart beat classification from ECG records Personalized Advance Warning for Cardiac Arrhythmia PhonoCardiogram (PCG) Anomaly Detection EED NEWSLETTER VOL. 2, ISSUE 1

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