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The growing demand for recording longer ECG signals to improve the effectiveness of IoT-enabled remote clinical healthcare is contributing large amounts of ECG data. While lossy compression techniques have shown potential in significantly lowering the amount of data, investigation on how to trade-off between data reduction and data fidelity on ECG data received relatively less attention. This paper gives insight into the power of lossy compression to ECG signals by balancing between data quality and compression ratio. We evaluate the performance of transformed-based lossy compressions on the ECG datasets collected from the Biosemi ActiveTwo devices. Our experimental results indicate that ECG data exhibit high energy compaction property through transformations like DCT and DWT, thus could improve compression ratios significantly without hurting data fidelity much. More importantly, we evaluate the effect of lossy compression on ECG signals by validating the R-peak in the QRS complex. Our method can obtain low error rates measured in PRD (as low as 0.3) and PSNR (up to 67) using only 5% of the transform coefficients. Therefore, R-peaks in the reconstructed ECG signals are almost identical to ones in the original signals, thus facilitating extended ECG monitoring. 

Edge devices with attentive sensors enable various intelligent services by exploring streams of sensor data. However, anomalies, which are inevitable due to faults or failures in the sensor and network, can result in incorrect or unwanted operational decisions. While promptly ensuring the accuracy of IoT data is critical, lack of labels for live sensor data and limited storage resources necessitates efficient and reliable detection of anomalies at edge nodes. Motivated by the existence of unique sparsity profiles that express original signals as a combination of a few coefficients between normal and abnormal sensing periods, we propose a novel anomaly detection approach, called ADSP (Anomaly Detection with Sparsity Profile). The key idea is to apply a transformation on the raw data, identify top-K dominant components that represent normal data behaviors, and detect data anomalies based on the disparity from K values approximating the periods of normal data in an unsupervised manner. Our evaluation using a set of synthetic datasets demonstrates that ADSP can achieve 92%–100% of detection accuracy. To validate our anomaly detection approach on real-world cases, we label potential anomalies using a range of error boundary conditions using sensors exhibiting a straight line in Q-Q plot and strong Pearson correlation and conduct a controlled comparison of the detection accuracy. Our experimental evaluation using real-world datasets demonstrates that ADSP can detect 83%– 92% of anomalies using only 1.7% of the original data, which is comparable to the accuracy achieved by using the entire datasets.