Despite the significant advancement in the healthcare sector in the past decade, chronic diseases such as cancer, cardiovascular disorders, diabetics, and neurological disorders still remain as the leading causes of death. Early detection of chronic diseases can help in reducing mortality, slowing disease progression, and mitigating complications to optimize the quality of life and decrease the demand on the health-care system. However, detecting chronic diseases from physiological signals such as PPG, EEG, ECG, etc. is challenging due to each patient's abnormal pattern being very different from each other. To detect such abnormal patterns in real time, the system must learn each patient's ictal pattern from the physiological signal traces through machine learning approaches, hence achieving a patient-specific disease detection. Since this new class of biomedical systems becomes more ubiquitous, the design and development of energy-efficient techniques gain more significance to allow a sustainable realization of the entire monitoring system.

This talk will cover the design strategies of energy-efficient System-on-Chip (SoC) biomedical devices. I will first explore the challenges, limitations and potential pitfalls in wearable interface circuit design, and strategies to overcome such issues. Moreover, I will describe on-chip energy-efficient digital processing techniques for the implementation of machine-learning algorithms for disease detection focusing on two examples, 1) depth of anesthesia monitoring for patients during surgery, and 2) wearable non-invasive glucose monitoring device. The talk will conclude with interesting aspects and opportunities that lie ahead.