6-1 Conclusion
In this work, a low power wavelet-based ECG delineator is proposed for mobile healthcare applications. The transmission energy of the wireless sensor node can be greatly reduced by sending the extracted features only, thus increasing the monitoring time. The proposed delineator detects the ECG fiducial points including the P, QRSon, R, QRSend, and T waves. With all important features extracted, 8 different categories of heart syndromes can be detected providing instant alarm or feedback to users.
The wavelet preprocess eliminates the noise interference for mobile monitoring.
With adaptive threshold and search window generation considering noise level, the proposed algorithm achieves the detection accuracy over 99% for all provided feature.
The algorithm is designed with storage optimization and avoiding complex operations, making it suitable for hardware implementation.
Because of the different occurrence of features in time axis, the search kernel is shared and event-triggered only when needed. Operating using the proposed 2-phase asynchronous ring structure, the search kernel can be turned ON and OFF with little latency without the needs of additional high frequency clocks, making it suitable for system integration. A design flow is built and tested using commercial CAD tool for the construction of asynchronous search kernel. The asynchronous design also enables the possibility for the delineator to operate under low supply voltage with the ability to combat the severe PVT variation. Implemented in UMC 90nm technology with voltage scaling to 0.5V, the overall power is 2.56μW for real time ECG transmission.
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Finally, a wireless sensor node prototype is constructed using microcontroller MSP430 with WIFI transmission to verify the concept and algorithm for real time mobile on-sensor ECG delineation.
6-2 Future Work
In the future, based on the extracted ECG feature, on-sensor syndrome classification can be added to the system to make the healthcare application more complete. Instant result can be feedback to the user including health status information and advices. The techniques and concepts used in this thesis such as the wavelet noise removal, adaptive threshold generation and event-triggered asynchronous search kernel can also be applied to different kinds of bio-signals such as electromyography (EMG) or blood pressure for robust and efficient human body monitoring system.
Asynchronous circuit is clearly one of the techniques to combat PVT variation in future designs when going for low supply voltage. We are looking forward to exploit more efficient asynchronous design, and analysis the performance in sub-threshold region. New design flow for more convenient asynchronous designs is always an interesting research field. With improved and reliable design flow, we will be able to apply asynchronous circuit to larger design, making sub-threshold design an easy option for circuit designer to achieve lowest possible power consumption.
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