NADH dehydrogenase-like complex L subunit improves salt tolerance by enhancing photosynthetic electron transport.

Cyclic electron transport (CET) around photosystem I (PSI) mediated by the NADH dehydrogenase-like (NDH) complex is closely related to plant salt tolerance. However, whether overexpression of a core subunit of the NDH complex affects the photosynthetic electron transport under salt stress is currently unclear. Here, we expressed the NDH complex L subunit (Ndhl) genes ZmNdhl1 and ZmNdhl2 from C4 plant maize (Zea mays) or OsNdhl from C3 plant rice (Oryza sativa) using a constitutive promoter in rice. Transgenic rice lines expressing ZmNdhl1, ZmNdhl2, or OsNdhl displayed enhanced salt tolerance, as indicated by greater plant height, dry weight, and leaf relative water content, as well as lower malondialdehyde content compared to wild-type plants under salt stress. Fluorescence parameters such as post-illumination rise (PIR), the prompt chlorophyll a fluorescence transient (OJIP), modulated 820-nm reflection (MR), and delayed chlorophyll a fluorescence (DF) remained relatively normal in transgenic plants during salt stress. These results indicate that expression of ZmNdhl1, ZmNdhl2, or OsNdhl increases cyclic electron transport activity, slows down damage to linear electron transport, alleviates oxidative damage to the PSI reaction center and plastocyanin, and reduces damage to electron transport on the receptor side of PSI in rice leaves under salt stress. Thus, expression of Ndhl genes from maize or rice improves salt tolerance by enhancing photosynthetic electron transport in rice. Maize and rice Ndhl genes played a similar role in enhancing salinity tolerance and avoiding photosynthetic damage.

Multimodal Mental Health Digital Biomarker Analysis From Remote Interviews Using Facial, Vocal, Linguistic, and Cardiovascular Patterns.

OBJECTIVE: Psychiatric evaluation suffers from subjectivity and bias, and is hard to scale due to intensive professional training requirements. In this work, we investigated whether behavioral and physiological signals, extracted from tele-video interviews, differ in individuals with psychiatric disorders. METHODS: Temporal variations in facial expression, vocal expression, linguistic expression, and cardiovascular modulation were extracted from simultaneously recorded audio and video of remote interviews. Averages, standard deviations, and Markovian process-derived statistics of these features were computed from 73 subjects. Four binary classification tasks were defined: detecting 1) any clinically-diagnosed psychiatric disorder, 2) major depressive disorder, 3) self-rated depression, and 4) self-rated anxiety. Each modality was evaluated individually and in combination. RESULTS: Statistically significant feature differences were found between psychiatric and control subjects. Correlations were found between features and self-rated depression and anxiety scores. Heart rate dynamics provided the best unimodal performance with areas under the receiver-operator curve (AUROCs) of 0.68-0.75 (depending on the classification task). Combining multiple modalities provided AUROCs of 0.72-0.82. CONCLUSION: Multimodal features extracted from remote interviews revealed informative characteristics of clinically diagnosed and self-rated mental health status. SIGNIFICANCE: The proposed multimodal approach has the potential to facilitate scalable, remote, and low-cost assessment for low-burden automated mental health services.

Disentangling Visual Exploration Differences in Cognitive Impairment.

OBJECTIVE: Individuals with cognitive impairment (CI) exhibit different oculomotor functions and viewing behaviors. In this work we aimed to quantify the differences in these functions with CI severity, and assess general CI and specific cognitive functions related to visual exploration behaviors. METHODS: A validated passive viewing memory test with eyetracking was administered to 348 healthy controls and CI individuals. Spatiotemporal properties of the scanpath, the semantic category of the viewed regions, and other composite features were extracted from the estimated eyegaze locations on the corresponding pictures displayed during the test. These features were then used to characterize viewing patterns, classify cognitive impairment, and estimate scores in various neuropsychological tests using machine learning. RESULTS: Statistically significant differences in spatial, spatiotemporal, and semantic features were found between healthy controls and individuals with CI. The CI group spent more time gazing at the center of the image, looked at more regions of interest (ROI), transitioned less often between ROI yet in a more unpredictable manner, and exhibited different semantic preferences. A combination of these features achieved an area under the receiver-operator curve of 0.78 in differentiating CI individuals from controls. Statistically significant correlations were identified between actual and estimated CI scores and other neuropsychological tests. CONCLUSION: Evaluating visual exploration behaviors provided quantitative and systematic evidence of differences in CI individuals, leading to an improved approach for passive cognitive impairment screening. SIGNIFICANCE: The proposed passive, accessible, and scalable approach could help with earlier detection and a better understanding of cognitive impairment.

Catalytic Structure Design by AI Generating with Spectroscopic Descriptors.

Generative artificial intelligence has depicted a beautiful blueprint for on-demand design in chemical research. However, the few successful chemical generations have only been able to implement a few special property values because most chemical descriptors are mathematically discrete or discontinuously adjustable. Herein, we use spectroscopic descriptors with machine learning to establish a quantitative spectral structure-property relationship for adsorbed molecules on metal monatomic catalysts. Besides catalytic properties such as adsorption energy and charge transfer, the complete spatial relative coordinates of the adsorbed molecule were successfully inverted. The spectroscopic descriptors and prediction models are generalized, allowing them to be transferred to several different systems. Due to the continuous tunability of the spectroscopic descriptors, the design of catalytic structures with continuous adsorption states generated by AI in the catalytic process has been achieved. This work paves the way for using spectroscopy to enable real-time monitoring of the catalytic process and continuous customization of catalytic performance, which will lead to profound changes in catalytic research.

Using HIPAA (Health Insurance Portability and Accountability Act)-Compliant Transcription Services for Virtual Psychiatric Interviews: Pilot Comparison Study.

BACKGROUND: Automatic speech recognition (ASR) technology is increasingly being used for transcription in clinical contexts. Although there are numerous transcription services using ASR, few studies have compared the word error rate (WER) between different transcription services among different diagnostic groups in a mental health setting. There has also been little research into the types of words ASR transcriptions mistakenly generate or omit. OBJECTIVE: This study compared the WER of 3 ASR transcription services (Amazon Transcribe [Amazon.com, Inc], Zoom-Otter AI [Zoom Video Communications, Inc], and Whisper [OpenAI Inc]) in interviews across 2 different clinical categories (controls and participants experiencing a variety of mental health conditions). These ASR transcription services were also compared with a commercial human transcription service, Rev (Rev.Com, Inc). Words that were either included or excluded by the error in the transcripts were systematically analyzed by their Linguistic Inquiry and Word Count categories. METHODS: Participants completed a 1-time research psychiatric interview, which was recorded on a secure server. Transcriptions created by the research team were used as the gold standard from which WER was calculated. The interviewees were categorized into either the control group (n=18) or the mental health condition group (n=47) using the Mini-International Neuropsychiatric Interview. The total sample included 65 participants. Brunner-Munzel tests were used for comparing independent sets, such as the diagnostic groupings, and Wilcoxon signed rank tests were used for correlated samples when comparing the total sample between different transcription services. RESULTS: There were significant differences between each ASR transcription service's WER (P<.001). Amazon Transcribe's output exhibited significantly lower WERs compared with the Zoom-Otter AI's and Whisper's ASR. ASR performances did not significantly differ across the 2 different clinical categories within each service (P>.05). A comparison between the human transcription service output from Rev and the best-performing ASR (Amazon Transcribe) demonstrated a significant difference (P<.001), with Rev having a slightly lower median WER (7.6%, IQR 5.4%-11.35 vs 8.9%, IQR 6.9%-11.6%). Heat maps and spider plots were used to visualize the most common errors in Linguistic Inquiry and Word Count categories, which were found to be within 3 overarching categories: Conversation, Cognition, and Function. CONCLUSIONS: Overall, consistent with previous literature, our results suggest that the WER between manual and automated transcription services may be narrowing as ASR services advance. These advances, coupled with decreased cost and time in receiving transcriptions, may make ASR transcriptions a more viable option within health care settings. However, more research is required to determine if errors in specific types of words impact the analysis and usability of these transcriptions, particularly for specific applications and in a variety of populations in terms of clinical diagnosis, literacy level, accent, and cultural origin.

Inverse design of chiral functional films by a robotic AI-guided system.

Artificial chiral materials and nanostructures with strong and tuneable chiroptical activities, including sign, magnitude, and wavelength distribution, are useful owing to their potential applications in chiral sensing, enantioselective catalysis, and chiroptical devices. Thus, the inverse design and customized manufacturing of these materials is highly desirable. Here, we use an artificial intelligence (AI) guided robotic chemist to accurately predict chiroptical activities from the experimental absorption spectra and structure/process parameters, and generate chiral films with targeted chiroptical activities across the full visible spectrum. The robotic AI-chemist carries out the entire process, including chiral film construction, characterization, and testing. A machine learned reverse design model using spectrum embedded descriptors is developed to predict optimal structure/process parameters for any targeted chiroptical property. A series of chiral films with a dissymmetry factor as high as 1.9 (gabs ~ 1.9) are identified out of more than 100 million possible structures, and their feasible application in circular polarization-selective color filters for multiplex laser display and switchable circularly polarized (CP) luminescence is demonstrated. Our findings not only provide chiral films with the highest reported chiroptical activity, but also have great fundamental value for the inverse design of chiroptical materials.

Multimodal mental health assessment with remote interviews using facial, vocal, linguistic, and cardiovascular patterns.

Objective: The current clinical practice of psychiatric evaluation suffers from subjectivity and bias, and requires highly skilled professionals that are often unavailable or unaffordable. Objective digital biomarkers have shown the potential to address these issues. In this work, we investigated whether behavioral and physiological signals, extracted from remote interviews, provided complimentary information for assessing psychiatric disorders. Methods: Time series of multimodal features were derived from four conceptual modes: facial expression, vocal expression, linguistic expression, and cardiovascular modulation. The features were extracted from simultaneously recorded audio and video of remote interviews using task-specific and foundation models. Averages, standard deviations, and hidden Markov model-derived statistics of these features were computed from 73 subjects. Four binary classification tasks were defined: detecting 1) any clinically-diagnosed psychiatric disorder, 2) major depressive disorder, 3) self-rated depression, and 4) self-rated anxiety. Each modality was evaluated individually and in combination. Results: Statistically significant feature differences were found between controls and subjects with mental health conditions. Correlations were found between features and self-rated depression and anxiety scores. Visual heart rate dynamics achieved the best unimodal performance with areas under the receiver-operator curve (AUROCs) of 0.68-0.75 (depending on the classification task). Combining multiple modalities achieved AUROCs of 0.72-0.82. Features from task-specific models outperformed features from foundation models. Conclusion: Multimodal features extracted from remote interviews revealed informative characteristics of clinically diagnosed and self-rated mental health status. Significance: The proposed multimodal approach has the potential to facilitate objective, remote, and low-cost assessment for low-burden automated mental health services.

Disentangling visual exploration differences in cognitive impairment.

Objective: Compared to individuals without cognitive impairment (CI), those with CI exhibit differences in both basic oculomotor functions and complex viewing behaviors. However, the characteristics of the differences and how those differences relate to various cognitive functions have not been widely explored. In this work we aimed to quantify those differences and assess general cognitive impairment and specific cognitive functions. Methods: A validated passive viewing memory test with eyetracking was administered to 348 healthy controls and CI individuals. Spatial, temporal, semantic, and other composite features were extracted from the estimated eye-gaze locations on the corresponding pictures displayed during the test. These features were then used to characterize viewing patterns, classify cognitive impairment, and estimate scores in various neuropsychological tests using machine learning. Results: Statistically significant differences in spatial, spatiotemporal, and semantic features were found between healthy controls and individuals with CI. CI group spent more time gazing at the center of the image, looked at more regions of interest (ROI), transitioned less often between ROI yet in a more unpredictable manner, and had different semantic preferences. A combination of these features achieved an area under the receiver-operator curve of 0.78 in differentiating CI individuals from controls. Statistically significant correlations were identified between actual and estimated MoCA scores and other neuropsychological tests. Conclusion: Evaluating visual exploration behaviors provided quantitative and systematic evidence of differences in CI individuals, leading to an improved approach for passive cognitive impairment screening. Significance: The proposed passive, accessible, and scalable approach could help with earlier detection and a better understanding of cognitive impairment.

Scalable, modular continuous wave functional near-infrared spectroscopy system (Spotlight).

Significance: We present a fiberless, portable, and modular continuous wave-functional near-infrared spectroscopy system, Spotlight, consisting of multiple palm-sized modules-each containing high-density light-emitting diode and silicon photomultiplier detector arrays embedded in a flexible membrane that facilitates optode coupling to scalp curvature. Aim: Spotlight's goal is to be a more portable, accessible, and powerful functional near-infrared spectroscopy (fNIRS) device for neuroscience and brain-computer interface (BCI) applications. We hope that the Spotlight designs we share here can spur more advances in fNIRS technology and better enable future non-invasive neuroscience and BCI research. Approach: We report sensor characteristics in system validation on phantoms and motor cortical hemodynamic responses in a human finger-tapping experiment, where subjects wore custom 3D-printed caps with two sensor modules. Results: The task conditions can be decoded offline with a median accuracy of 69.6%, reaching 94.7% for the best subject, and at a comparable accuracy in real time for a subset of subjects. We quantified how well the custom caps fitted to each subject and observed that better fit leads to more observed task-dependent hemodynamic response and better decoding accuracy. Conclusions: The advances presented here should serve to make fNIRS more accessible for BCI applications.

An investigation of privacy preservation in deep learning-based eye-tracking.

BACKGROUND: The expanding usage of complex machine learning methods such as deep learning has led to an explosion in human activity recognition, particularly applied to health. However, complex models which handle private and sometimes protected data, raise concerns about the potential leak of identifiable data. In this work, we focus on the case of a deep network model trained on images of individual faces. MATERIALS AND METHODS: A previously published deep learning model, trained to estimate the gaze from full-face image sequences was stress tested for personal information leakage by a white box inference attack. Full-face video recordings taken from 493 individuals undergoing an eye-tracking- based evaluation of neurological function were used. Outputs, gradients, intermediate layer outputs, loss, and labels were used as inputs for a deep network with an added support vector machine emission layer to recognize membership in the training data. RESULTS: The inference attack method and associated mathematical analysis indicate that there is a low likelihood of unintended memorization of facial features in the deep learning model. CONCLUSIONS: In this study, it is showed that the named model preserves the integrity of training data with reasonable confidence. The same process can be implemented in similar conditions for different models.

Chiral biosensing at both interband transition and plasmonic extinction regions using twisted-stacked nanowire arrays.

Chiral metal nanostructures that exhibit strong chiroptical properties and enhanced light-matter interactions have recently attracted great interest due to their potential applications including chiral sensing and asymmetric synthesis. Most studies in this field focused on chiral sensing using circular dichroism (CD) responses at the plasmonic extinction region. In comparison, little is known about their CD responses at interband transition regions and their utility in chiral biosensing. Herein, we constructed a series of twisted-stacked silver nanowire arrays (TNAs) featuring CD signals at both the interband transition and plasmonic extinction regions and that are independently controllable. These TNAs are highly sensitive towards protein secondary structures. Proteins containing more ß-sheets are more sensitive toward strong chiral plasmonic fields, whereas proteins rich in α-helices tend to generate larger CD shifts at the interband transition region of TNAs. The mutually independent optical activities at the interband transition and plasmonic extinction regions complement each other, providing more sensitivity and reliability in chiral biosensing.

Multimodal Assessment of Schizophrenia and Depression Utilizing Video, Acoustic, Locomotor, Electroencephalographic, and Heart Rate Technology: Protocol for an Observational Study.

BACKGROUND: Current standards of psychiatric assessment and diagnostic evaluation rely primarily on the clinical subjective interpretation of a patient's outward manifestations of their internal state. While psychometric tools can help to evaluate these behaviors more systematically, the tools still rely on the clinician's interpretation of what are frequently nuanced speech and behavior patterns. With advances in computing power, increased availability of clinical data, and improving resolution of recording and sensor hardware (including acoustic, video, accelerometer, infrared, and other modalities), researchers have begun to demonstrate the feasibility of cutting-edge technologies in aiding the assessment of psychiatric disorders. OBJECTIVE: We present a research protocol that utilizes facial expression, eye gaze, voice and speech, locomotor, heart rate, and electroencephalography monitoring to assess schizophrenia symptoms and to distinguish patients with schizophrenia from those with other psychiatric disorders and control subjects. METHODS: We plan to recruit three outpatient groups: (1) 50 patients with schizophrenia, (2) 50 patients with unipolar major depressive disorder, and (3) 50 individuals with no psychiatric history. Using an internally developed semistructured interview, psychometrically validated clinical outcome measures, and a multimodal sensing system utilizing video, acoustic, actigraphic, heart rate, and electroencephalographic sensors, we aim to evaluate the system's capacity in classifying subjects (schizophrenia, depression, or control), to evaluate the system's sensitivity to within-group symptom severity, and to determine if such a system can further classify variations in disorder subtypes. RESULTS: Data collection began in July 2020 and is expected to continue through December 2022. CONCLUSIONS: If successful, this study will help advance current progress in developing state-of-the-art technology to aid clinical psychiatric assessment and treatment. If our findings suggest that these technologies are capable of resolving diagnoses and symptoms to the level of current psychometric testing and clinician judgment, we would be among the first to develop a system that can eventually be used by clinicians to more objectively diagnose and assess schizophrenia and depression with the possibility of less risk of bias. Such a tool has the potential to improve accessibility to care; to aid clinicians in objectively evaluating diagnoses, severity of symptoms, and treatment efficacy through time; and to reduce treatment-related morbidity. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36417.

An Edge Computing and Ambient Data Capture System for Clinical and Home Environments.

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (1) Estimating occupancy and human activity phenotyping; (2) Medical equipment alarm classification; (3) Geolocation of humans in a built environment; (4) Ambient light logging; and (5) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.

Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review.

BACKGROUND: Schizophrenia is a severe psychiatric disorder that causes significant social and functional impairment. Currently, the diagnosis of schizophrenia is based on information gleaned from the patient's self-report, what the clinician observes directly, and what the clinician gathers from collateral informants, but these elements are prone to subjectivity. Utilizing computer vision to measure facial expressions is a promising approach to adding more objectivity in the evaluation and diagnosis of schizophrenia. METHOD: We conducted a systematic review using PubMed and Google Scholar. Relevant publications published before (including) December 2021 were identified and evaluated for inclusion. The objective was to conduct a systematic review of computer vision for facial behavior analysis in schizophrenia studies, the clinical findings, and the corresponding data processing and machine learning methods. RESULTS: Seventeen studies published between 2007 to 2021 were included, with an increasing trend in the number of publications over time. Only 14 articles used interviews to collect data, of which different combinations of passive to evoked, unstructured to structured interviews were used. Various types of hardware were adopted and different types of visual data were collected. Commercial, open-access, and in-house developed models were used to recognize facial behaviors, where frame-level and subject-level features were extracted. Statistical tests and evaluation metrics varied across studies. The number of subjects ranged from 2-120, with an average of 38. Overall, facial behaviors appear to have a role in estimating diagnosis of schizophrenia and psychotic symptoms. When studies were evaluated with a quality assessment checklist, most had a low reporting quality. CONCLUSION: Despite the rapid development of computer vision techniques, there are relatively few studies that have applied this technology to schizophrenia research. There was considerable variation in the clinical paradigm and analytic techniques used. Further research is needed to identify and develop standardized practices, which will help to promote further advances in the field.

Automated analysis of facial emotions in subjects with cognitive impairment.

Differences in expressing facial emotions are broadly observed in people with cognitive impairment. However, these differences have been difficult to objectively quantify and systematically evaluate among people with cognitive impairment across disease etiologies and severity. Therefore, a computer vision-based deep learning model for facial emotion recognition trained on 400.000 faces was utilized to analyze facial emotions expressed during a passive viewing memory test. In addition, this study was conducted on a large number of individuals (n = 493), including healthy controls and individuals with cognitive impairment due to diverse underlying etiologies and across different disease stages. Diagnoses included subjective cognitive impairment, Mild Cognitive Impairment (MCI) due to AD, MCI due to other etiologies, dementia due to Alzheimer's diseases (AD), and dementia due to other etiologies (e.g., Vascular Dementia, Frontotemporal Dementia, Lewy Body Dementia, etc.). The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive performance across all participants. A participant with a score of less than or equal to 24 was considered cognitively impaired (CI). Compared to cognitively unimpaired (CU) participants, CI participants expressed significantly less positive emotions, more negative emotions, and higher facial expressiveness during the test. In addition, classification analysis revealed that facial emotions expressed during the test allowed effective differentiation of CI from CU participants, largely independent of sex, race, age, education level, mood, and eye movements (derived from an eye-tracking-based digital biomarker for cognitive impairment). No screening methods reliably differentiated the underlying etiology of the cognitive impairment. The findings provide quantitative and comprehensive evidence that the expression of facial emotions is significantly different in people with cognitive impairment, and suggests this may be a useful tool for passive screening of cognitive impairment.

Efficient and accurate identification of ear diseases using an ensemble deep learning model.

Early detection and appropriate medical treatment are of great use for ear disease. However, a new diagnostic strategy is necessary for the absence of experts and relatively low diagnostic accuracy, in which deep learning plays an important role. This paper puts forward a mechanic learning model which uses abundant otoscope image data gained in clinical cases to achieve an automatic diagnosis of ear diseases in real time. A total of 20,542 endoscopic images were employed to train nine common deep convolution neural networks. According to the characteristics of the eardrum and external auditory canal, eight kinds of ear diseases were classified, involving the majority of ear diseases, such as normal, Cholestestoma of the middle ear, Chronic suppurative otitis media, External auditory cana bleeding, Impacted cerumen, Otomycosis external, Secretory otitis media, Tympanic membrane calcification. After we evaluate these optimization schemes, two best performance models are selected to combine the ensemble classifiers with real-time automatic classification. Based on accuracy and training time, we choose a transferring learning model based on DensNet-BC169 and DensNet-BC1615, getting a result that each model has obvious improvement by using these two ensemble classifiers, and has an average accuracy of 95.59%. Considering the dependence of classifier performance on data size in transfer learning, we evaluate the high accuracy of the current model that can be attributed to large databases. Current studies are unparalleled regarding disease diversity and diagnostic precision. The real-time classifier trains the data under different acquisition conditions, which is suitable for real cases. According to this study, in the clinical case, the deep learning model is of great use in the early detection and remedy of ear diseases.

Classifying Major Depressive Disorder and Response to Deep Brain Stimulation Over Time by Analyzing Facial Expressions.

OBJECTIVE: Major depressive disorder (MDD) is a common psychiatric disorder that leads to persistent changes in mood and interest among other signs and symptoms. We hypothesized that convolutional neural network (CNN) based automated facial expression recognition, pre-trained on an enormous auxiliary public dataset, could provide improve generalizable approach to MDD automatic assessment from videos, and classify remission or response to treatment. METHODS: We evaluated a novel deep neural network framework on 365 video interviews (88 hours) from a cohort of 12 depressed patients before and after deep brain stimulation (DBS) treatment. Seven basic emotions were extracted with a Regional CNN detector and an Imagenet pre-trained CNN, both of which were trained on large-scale public datasets (comprising over a million images). Facial action units were also extracted with the Openface toolbox. Statistics of the temporal evolution of these image features over each recording were extracted and used to classify MDD remission and response to DBS treatment. RESULTS: An Area Under the Curve of 0.72 was achieved using leave-one-subject-out cross-validation for remission classification and 0.75 for response to treatment. CONCLUSION: This work demonstrates the potential for the classification of MDD remission and response to DBS treatment from passively acquired video captured during unstructured, unscripted psychiatric interviews. SIGNIFICANCE: This novel MDD evaluation could be used to augment current psychiatric evaluations and allow automatic, low-cost, frequent use when an expert isn't readily available or the patient is unwilling or unable to engage. Potentially, the framework may also be applied to other psychiatric disorders.

An Energy Efficient AdaBoost Cascade Method for Long-Term Seizure Detection in Portable Neurostimulators.

Responsive neurostimulation (RNS) is becoming a promising therapy in refractory epilepsy control. In a RNS system, a critical challenge is how to detect seizure onsets accurately with low computational costs. In this study, an energy efficient AdaBoost cascade method for robust long-term seizure detection from local field potential (LFP) signals was proposed and evaluated in a portable neurostimulator. The AdaBoost cascade method included two stages: a seizure candidate detection stage (stage1) and a false alarm rejection stage (stage2). Since seizure activities occurred occasionally in most cases, most normal signal segments can be efficiently classified in stage1. A small percent of suspicious segments were fed into stage2 for more strict examination, where more sophisticated features were extracted to precisely identify seizure activities and reduce false alarms. To further optimize energy efficiency for hardware implementation, we proposed a soft-cascade algorithm for stage2 to reduce the computational costs. Our method was implemented in a generalized neurostimulator and evaluated online with four rats with chronic temporal lobe epilepsy (TLE). A total of 2280.1 hours of LFP signals were recorded and analyzed. Our approach achieves a detection rate of 91.6%, 3.85/h false alarm rate, and 2.31 second detection delay. With the two-stage cascade approach, 98.13% of computational costs could be reduced, with respect to the time of calculation of all features. Our method can detect seizure onsets precisely with high energy efficiency, which is suitable for hardware implementation in portable neuro-stimulators. Therefore, this proposed approach is promising to provide effective and robust performances in long-term seizure detection in neurostimulators for responsive seizure control.

Acute Seizure Control Efficacy of Multi-Site Closed-Loop Stimulation in a Temporal Lobe Seizure Model.

The closed-loop electrical stimulation is emerging as a promising neural modulation therapy for refractory epilepsy. However, the efficacy of electrical stimulation is less than optimal and the mechanism of seizure control is still unclear. In this paper, we evaluated the acute seizure control efficacy of the multi-site closed-loop stimulation (MSCLS) in a rodent model with a custom designed closed-loop neurostimulator. A total of 18 rats were injected with kainic-acid in CA3 of the left hippocampus to induce acute temporal lobe seizures. Instead of single target stimulation, four target sites in left hemisphere including CA1 and CA3 of the hippocampus, sub-thalamic nucleus, and M1 region of the motor cortex were selected for both recording and stimulation. A low-cost efficient multi-site seizure detection algorithm was implemented in the neurostimulator for MSCLS. With MSCLS treatment, the rats without status-epilepsy (SE) significantly reduced the seizure duration and the number of generalized seizures in each site. When considering the rats developed SE, the MSCLS could also alleviate the seizure severity, but had little effect on the seizure duration and seizure number. In conclusion, although the efficacy of MSCLS was still limited by the stimulation sites, stimulation parameters, and seizure model chosen in this paper, the MSCLS itself would be a promising direction for the refractory seizure treatment.