Real-Time Anomaly Detection of Continuously Monitored Periodic Bio-Signals Like ECG

In this paper, we proposed an efficient heuristic algorithm for real-time anomaly detection of periodic bio-signals. We introduced a new concept, "mother signal" which is the average of normal subsequences of one period length. Their number is overwhelmingly large compared to anomalies. From the time series, first we find the fundamental time period, assuming the period to be stable over the whole time. Next, we find the normal subsequence of length equal to time-period and call it the "mother signal". When the distance of a subsequence of same length is large from the mother signal, we identify it as anomaly. While calculating the distance, we ensure that it is not large due to time shift. To ensure that, we shift-and-rotate the subsequence in step of one slot at a time and find the minimum distance of all such comparisons. The proposed heuristic algorithm using mother signal is efficient. Results are compared and found to be similar to that obtained using brute force comparisons of all possible pairs. Computational costs are compared to show that the proposed method is more efficient compared to existing works.