Ultra-Low Power QRS Detection and ECG Compression Architecture for IoT Healthcare Devices

An ultra-low power electrocardiogram (ECG) processing architecture with an adequate level of accuracy is a necessity for Internet of Things (IoT) medical wearable devices. This paper presents a novel real-time QRS detector and an ECG compression architecture for IoT healthcare devices. An absolute-value curve length transform (A-CLT) is proposed that effectively enhances the QRS complex detection with minimized hardware resources. The proposed architecture requires adders, shifters, and comparators only, and removes the need for any multipliers. QRS detection was accomplished by using adaptive thresholds in the A-CLT transformed ECG signal, and achieved a sensitivity of 99.37% and the predictivity of 99.38% when validated using Physionet ECG database. Furthermore, a lossless compression technique was incorporated into the proposed architecture that uses the ECG signal first derivative and entropy encoding. An average compression ratio of 2.05 was achieved when evaluated using MIT-BIH database. The proposed QRS detection architecture deals with almost all the ECG signal artifacts, such as low-frequency noise, baseline drift, and high-frequency interference with minimum hardware resources. The proposed QRS architecture was synthesized using 65-nm low-power process using standard-cell-based flow. The power consumption of the design was 6.5 nW while operating at a supply of 1 V and a frequency of 250 Hz. Moreover, the system could benefit from duty-cycling.