Wireless medical technologies have created opportunities for new methods of preventive care using biomedical implanted and body-worn devices. The design of the technologies that will enable these applications requires correct delivery of the vital physiological signs of the patient along with the energy management in power-constrained devices. The high cost and even higher risk of battery replacement require that these devices be designed and developed for minimum energy consumption.
In this research, we explore a variety of ultra low-power DSP techniques for wearable biomedical devices. A blend of feature engineering and machine learning algorithms are employed and evaluated within the context of real-time classification applications. The evaluation is based on two main criteria: 1. classification accuracy and 2. algorithmic complexity (computation, memory, latency). Currently, two case studies are being explored.
The first case study is the detection of seizures for epileptic patients using multi-physiological signals in an ambulatory setting. In a clinical setting, the de facto gold standard for the detection of seizures is through EEG+Video monitoring. In an ambulatory setting, video is less viable and EEG can only be done using limited electrodes with higher noise margins. This work looks at incorporating other physiological signals such as ECG, respiratory, and motion to boost classification performance in an ambulatory setting where noise is more prone. These multi-physiological signals will be fed into a deep neural network to perform both the multi-variate feature abstraction as well as seizure classification.
The second case study is an assistive technology that enables a user to interactive with their surroundings using a tongue-driven interface. The Tongue Drive System (TDS) is a wearable device that allows for realtime tracking of the tongue motion in the oral space by sensing the changes in the magnetic field generated by a small magnetic marker attached to a users tongue. Four strategically placed 3D magnetometers are used to measure the magnetic field. Efficient DSP and machine learning algorithms are devised to remove noise and artifacts such as Earth's magnetic field as well as to triangulate the location of the magnet locally on the sensor node.