Objective: Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring. Methods: Changes in sleep condition affect the autonomic nervous system (ANS) and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram (ECG) trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control (MSPC) which is a well-known anomaly detection method. Result: The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 drowsy episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour. Conclusion: The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction. Significance: The proposed method can contribute to prevent accidents caused by drowsy driving.

A warning prior to seizure onset can help improve the quality of life for epilepsy patients. The feasibility of a wearable system for predicting epileptic seizures using anomaly detection based on machine learning is evaluated. An original telemeter is developed for continuous measurement of R-R intervals derived from an electrocardiogram. A bespoke smartphone app calculates the indices of heart rate variability in real time from the R-R intervals, and the indices are monitored using multivariate statistical process control by the smartphone app. The proposed system was evaluated on seven epilepsy patients. The accuracy and reliability of the R-R interval measurement, which was examined in comparison with the reference electrocardiogram, showed sufficient performance for heart rate variability analysis. The results obtained using the proposed system were compared with those obtained using the existing video and electroencephalogram assessments; it was noted that the proposed method has a sensitivity of 85.7% in detecting heart rate variability change prior to seizures. The false positive rate of 0.62 times/h was not significantly different from the healthy controls. The prediction performance and practical advantages of portability and real-time operation are demonstrated in this study.

Although early reperfusion therapy is effective for acute ischemic stroke, limited therapeutic time-window resulted in only 10% of patients receiving reperfusion therapy. A fast and reliable stroke detection method is desired so that patients can receive early reperfusion therapy. It has been reported that ischemic stroke affects heart rate variability (HRV), which reflects activities of the autonomic nervous function. Thus, ischemic stroke may be detected at an acute stage through monitoring HRV. This paper proposes an HRV-based ischemic stroke detection algorithm by using multivariate statistical process control (MSPC), which is a well-known anomaly detection algorithm. As a feasibility study before collecting a large amount of clinical data from human patients, this paper used the middle cerebral artery occlusion (MCAO) model in rats for collecting HRV data shortly after ischemic stroke onsets. The 11 MCAO-operated rats and 11 sham-operated rats were prepared, and HRV data of three sham-operated rats were used for model construction. The data on the other 19 rats were used for its validation. The experimental result showed that sensitivity and specificity of the proposed algorithm were 82% and 75%, respectively. Thus, the present work shows the possibility of realizing an HRV-based ischemic stroke detection system for human patients.

A focal brain cooling system for treatment of refractory epilepsy that is implantable and wearable may permit patients with this condition to lead normal daily lives. We have developed such a system for cooling of the epileptic focus by delivery of cold saline to a cooling device that is implanted cranially. The outflow is pumped for circulation and cooled by a Peltier device. Here, we describe the design of the system and evaluate its feasibility by simulation. Mathematical models were constructed based on equations of fluid dynamics and data from a cat model. Computational fluid dynamics simulations gave the following results: 1) a cooling device with a complex channel structure gives a more uniform temperature in the brain; 2) a cooling period of <;10 min is required to reach an average temperature of 25.0°C at 2 mm below the brain surface, which is the target temperature for seizure suppression. This time is short enough for cooling of the brain before seizure onset after seizure prediction by an intracranial electroencephalogram-based algorithm; and 3) battery charging would be required once every several days for most patients. These results suggest that the focal brain cooling system may be clinically applicable.

Objective: The purpose of this paper is to demonstrate how the integration of the multi-channel measurement capabilities of near-infrared spectroscopy (NIRS), electrocorticography (ECoG), and negative temperature coefficient thermistor sensors into a single device compact enough for subdural implantation can provide beneficial information on various aspects of brain cortical activity and prove a powerful medical modality for pre-, intra-, and post-operative diagnoses in neurosurgery. Methods: The development of a flexible multi-modal multi-channel probe for the simultaneous measurement of the NIRS, ECoG, and surficial temperature obtained from the cerebral cortex was carried out. Photoelectric bare chips for NIRS channels, miniature temperature-coefficient thermistors for measuring localized temperature variation, and 3-mm-diameter platinum plates for ECoG recording were assembled on a polyimide-based flexible printed circuit to create six channels for each modality. A conformal coating of Parylene-C was applied on all the channels except the ECoG to make the probe surface biocompatible. Results: As a first-in-human study, the simultaneous measurement capability of the multi-modality probe, with sufficient signal-to-noise ratio and accuracy, to observe pathological neural activities in subjects during surgery and post-operative monitoring, with no complications two weeks since the implantation, was confirmed. Conclusion: The feasibility of using a single device to assess the dynamic pathological activity from three different aspects was determined for human patients. Significance: The simultaneous and accurate multi-channel recording of electrical, hemodynamic, and thermographic cortical activities in a single device small enough for subdural implantation is likely to have major implications in neurosurgery and neuroscience.

Various drowsiness detection systems have been developed to prevent traffic accidents. Drivers feel such systems are annoying or useless when the systems provide many false drowsiness alarms. Drowsy driving prevention systems should satisfy the following three features to be accepted by many drivers: 1) Precise drowsiness detection, 2) Drowsy driving warning whereby drivers are less annoyed by false alarms, and 3) Mechanisms that promote drivers to adopt active behavior to maintain his/her wakefulness. The present work proposes a new drowsy driving prevention system that has these features. The proposed system, referred to as a wakefulness keeping support system (WKSS), consists of a drowsiness detection system (DDS), which corresponds to the first feature, and an active game system (AGS), which corresponds to the second and third features. The AGS is a simple game, which encourages drivers to adopt specific active behavior to play the game. Drivers can keep their wakefulness by playing the AGS, while enjoying it, even though false drowsiness alarms may occur. The usefulness of the proposed WKSS was evaluated through experiments using a driving simulator, which suggest that WKSS has a potential for being accepted by many drivers and contributing to realizing a zero-traffic accident society.