Sensor selection for energy-efficient ambulatory medical monitoring

Epilepsy affects over three million Americans of all ages. Despite recent advances, more than 20% of individuals with epilepsy never achieve adequate control of their seizures. The use of a small, portable, non-invasive seizure monitor could benefit these individuals tremendously. However, in order for such a device to be suitable for long-term wear, it must be both comfortable and lightweight. Typical state-of-the-art non-invasive seizure onset detection algorithms require 21 scalp electrodes to be placed on the head. These electrodes are used to generate 18 data streams, called channels. The large number of electrodes is inconvenient for the patient and processing 18 channels can consume a considerable amount of energy, a problem for a battery-powered device. In this paper, we describe an automated way to construct detectors that use fewer channels, and thus fewer electrodes. Starting from an existing technique for constructing 18 channel patient-specific detectors, we use machine learning to automatically construct reduced channel detectors. We evaluate our algorithm on data from 16 patients used in an earlier study. On average, our algorithm reduced the number of channels from 18 to 4.6 while decreasing the mean fraction of seizure onsets detected from 99% to 97%. For 12 out of the 16 patients, there was no degradation in the detection rate. While the average detection latency increased from 7.8 s to 11.2 s, the average rate of false alarms per hour decreased from 0.35 to 0.19. We also describe a prototype implementation of a single channel EEG monitoring device built using off-the-shelf components, and use this implementation to derive an energy consumption model. Using fewer channels reduced the average energy consumption by 69%, which amounts to a 3.3x increase in battery lifetime. Finally, we show how additional energy savings can be realized by using a low-power screening detector to rule out segments of data that are obviously not seizures. Though this technique does not reduce the number of electrodes needed, it does reduce the energy consumption by an additional 16%

A micropower support vector machine based seizure detection architecture embedded medical devices

Implantable neurostimulators for the treatment of epilepsy that are capable of sensing seizures can enable novel therapeutic applications. However, detecting seizures is challenging due to significant intracranial EEG signal variability across patients. In this paper, we illustrate how a machine-learning based, patient-specific seizure detector provides better performance and lower power consumption than a patient non-specific detector using the same seizure library. The machine-learning based architecture was fully implemented in the micropower domain, demonstrating feasibility for an embedded detector in implantable systems

A Micro-power EEG acquisition SoC with integrated seizure detection processor for continuous patient monitoring

Continuous on-scalp EEG monitoring provides a non-invasive means to detect the onset of seizures in epilepsy patients, but cables from the scalp pose a severe strangulation hazard during convulsions. Since the power of transmitting the EEG wirelessly is prohibitive, a complete SoC is presented, performing lowpower EEG acquisition, digitization, and local digital-processing to extract detection features, reducing the transmission-rate by 43x. To maximize power-efficiency, the acquisition LNA operates at the lowest reported VDD (of 1V, drawing 3.5muW), but is able to reject offsets (characteristic of metal-electrodes) that are even larger than the supply voltage. Importantly, its topology simultaneously optimizes noise-efficiency and input-impedance to maximize electrode signal-integrity, and it uses switch-capacitor transformers to improve the noise and manufactureabilty of large on-chip resistors. The complete SoC generates EEG featurevectors every 2sec, consuming a total of 9muJ per feature-vector.Intel Foundation (Ph.D. Fellowship Program)Natural Sciences and Engineering Research Council of CanadaMassachusetts Institute of Technology. Center for Integrated Circuits and SystemsNational Science Council of Taiwa

Management of endodontic-periodontic lesion of a maxillary lateral incisor with palatoradicular groove

Presence of palatal radicular grooves are considered to be an important contributing factor to the development of localized periodontitis, as it favored the accumulation and proliferation of bacterial plaque deep into the periodontium. Pulp involvement could result due to the introduction of bacterial toxins through channels that existed between the root canal system and the groove. Early diagnosis, elimination of inflammation and correction of anatomic complications are the key to a favorable outcome for managing palatoradicular groove. Present report describes successful management with an interdisciplinary approach of maxillary lateral incisor with combined endodontic periodontic lesion associated with palatoradicular groove

A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System

This paper presents a low-power SoC that performs EEG acquisition and feature extraction required for continuous detection of seizure onset in epilepsy patients. The SoC corresponds to one EEG channel, and, depending on the patient, up to 18 channels may be worn to detect seizures as part of a chronic treatment system. The SoC integrates an instrumentation amplifier, ADC, and digital processor that streams features-vectors to a central device where seizure detection is performed via a machine-learning classifier. The instrumentation-amplifier uses chopper-stabilization in a topology that achieves high input-impedance and rejects large electrode-offsets while operating at 1 V; the ADC employs power-gating for low energy-per-conversion while using static-biasing for comparator precision; the EEG feature extraction processor employs low-power hardware whose parameters are determined through validation via patient data. The integration of sensing and local processing lowers system power by 14à by reducing the rate of wireless EEG data transmission. Feature vectors are derived at a rate of 0.5 Hz, and the complete one-channel SoC operates from a 1 V supply, consuming 9 ¿ J per feature vector