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A novel wearable electrocardiograph (ECG) QRS detection algorithm for wearable ECG devices in body area networks is presented in this paper, which utilizes the multistage multiscale mathematical morphology filtering to suppress the impulsive noise and uses the multiframe differential modulus accumulation to remove the baseline drift and enhance the signal. The proposed algorithm, verified with data from the MIT/BIH Arrhythmia Database and wearable ECG devices, achieves an average QRS detection rate of 99.61%, a sensitivity of 99.81%, and a positive prediction of 99.80%. It compares favorably to the published methods.

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This paper presents a novel electrocardiogram (ECG) processing technique for joint data compression and QRS detection in a wireless wearable sensor. The proposed algorithm is aimed at lowering the average complexity per task by sharing the computational load among multiple essential signal-processing tasks needed for wearable devices. The compression algorithm, which is based on an adaptive linear data prediction scheme, achieves a lossless bit compression ratio of 2.286x. The QRS detection algorithm achieves a sensitivity (Se) of 99.64% and positive prediction (+P) of 99.81% when tested with the MIT/BIH Arrhythmia database. Lower overall complexity and good performance renders the proposed technique suitable for wearable/ambulatory ECG devices.

The non-selective cationic transient receptor canonical 6 (TRPC6) channels are involved in synaptic plasticity changes ranging from dendritic growth, spine morphology changes and increase in excitatory synapses. We previously showed that the TRPC6 activator hyperforin, the active antidepressant component of St. John's wort, induces neuritic outgrowth and spine morphology changes in PC12 cells and hippocampal CA1 neurons. However, the signaling cascade that transmits the hyperforin-induced transient rise in intracellular calcium into neuritic outgrowth is not yet fully understood. Several signaling pathways are involved in calcium transient-mediated changes in synaptic plasticity, ranging from calmodulinmediated Ras-induced signaling cascades comprising the mitogen-activated protein kinase, PI3K signal transduction pathways as well as Ca 2+ /calmodulin-dependent protein kinase II (CAMKII) and CAMKIV. We show that several mechanisms are involved in TRPC6-mediated synaptic plasticity changes in PC12 cells and primary hippocampal neurons. Influx of calcium via TRPC6 channels activates different pathways including Ras/mitogen-activated protein kinase/extracellular signal-regulated kinases, phosphatidylinositide 3-kinase/protein kinase B, and CAMKIV in both cell types, leading to cAMP-response element binding protein phosphorylation. These findings are interesting not only in terms of the downstream targets of TRPC6 channels but also because of their potential to facilitate further understanding of St. John's wort extract-mediated antidepressant activity.

In this paper, an asynchronous analog-to-information conversion system is introduced for measuring the RR intervals of the electrocardiogram (ECG) signals. The system contains a modified level-crossing analog-to-digital converter and a novel algorithm for detecting the R-peaks from the level-crossing sampled data in a compressed volume of data. Simulated with MIT-BIH Arrhythmia Database, the proposed system delivers an average detection accuracy of 98.3%, a sensitivity of 98.89%, and a positive prediction of 99.4%. Synthesized in 0.13 μm CMOS technology with a 1.2 V supply voltage, the overall system consumes 622 nW with core area of 0.136 mm (2), which make it suitable for wearable wireless ECG sensors in body-sensor networks.

This paper presents a novel data compression and transmission scheme for power reduction in Internet-of-Things (IoT) enabled wireless sensors. In the proposed scheme, data is compressed with both lossy and lossless techniques, so as to enable hybrid transmission mode, support adaptive data rate selection and save power in wireless transmission. Applying the method to electrocardiogram (ECG), the data is first compressed using a lossy compression technique with a high compression ratio (CR). The residual error between the original data and the decompressed lossy data is preserved using entropy coding, enabling a lossless restoration of the original data when required. Average CR of 2.1 × and 7.8 × were achieved for lossless and lossy compression respectively with MIT/BIH database. The power reduction is demonstrated using a Bluetooth transceiver and is found to be reduced to 18% for lossy and 53% for lossless transmission respectively. Options for hybrid transmission mode, adaptive rate selection and system level power reduction make the proposed scheme attractive for IoT wireless sensors in healthcare applications.