Simanto Saha

Suicidal Ideation Is Associated with Altered Variability of Fingertip Photo-Plethysmogram Signal in Depressed Patients

Physiological and psychological underpinnings of suicidal behavior remain ill-defined and lessen timely diagnostic identification of this subgroup of patients. Arterial stiffness is associated with autonomic dysregulation and may be linked to major depressive disorder (MDD). The aim of this study was to investigate the association between arterial stiffness by photo-plethysmogram (PPG) in MDD with and without suicidal ideation (SI) by applying multiscale tone entropy (T-E) variability analysis. Sixty-one 10-min PPG recordings were analyzed from 29 control, 16 MDD patients with (MDDSI+) and 16 patients without SI (MDDSI−). MDD was based on a psychiatric evaluation and the Mini-International Neuropsychiatric Interview (MINI). Severity of depression was assessed using the Hamilton Depression Rating Scale (HAM-D). PPG features included peak (systole), trough (diastole), pulse wave amplitude (PWA), pulse transit time (PTT) and pulse wave velocity (PWV). Tone (Diastole) at all lags and Tone (PWA) at lags 8, 9, and 10 were found to be significantly different between the MDDSI+ and MDDSI− group. However, Tone (PWA) at all lags and Tone (PTT) at scales 3–10 were also significantly different between the MDDSI+ and CONT group. In contrast, Entropy (Systole), Entropy (Diastole) and Tone (Diastole) were significantly different between MDDSI− and CONT groups. The suicidal score was also positively correlated (r = 0.39 ~ 0.47; p < 0.05) with systolic and diastolic entropy values at lags 2–10. Multivariate logistic regression analysis and leave-one-out cross-validation were performed to study the effectiveness of multi-lag T–E features in predicting SI risk. The accuracy of predicting SI was 93.33% in classifying MDDSI+ and MDDSI− with diastolic T-E and lag between 2 and 10. After including anthropometric variables (Age, body mass index, and Waist Circumference), that accuracy increased to 96.67% for MDDSI+/− classification. Our findings suggest that tone-entropy based PPG variability can be used as an additional accurate diagnostic tool for patients with depression to identify SI.

Reduced variability in pulse wave velocity in depressed patients with suicidal ideation

Recently arterial stiffness was found to be associated with depression. The aim of this study was to investigate the association between arterial pulse wave velocity (PWV) and Major Depressive Disorder (MDD) with or without suicidal ideation. Twenty unmedicated MDD patients with a history of suicidal ideations (Age: 32.37±9.53 years) and 20 unmedicated MDD patients without any history of suicidal ideations (Age: 36.84±8.66) were recruited for this study at a psychiatric clinic in the UAE. Depression severity was assessed with the Hamilton Depression Rating Scale and the Beck Depression Inventory. Pulse wave velocity (PWV) was estimated from the ratio of half of the height and Pulse Transit Time which is defined as the time delay between the R-wave of the ECG and the arrival of the pulse wave in the index finger respectively. MDD Patients with suicidal ideation were found to have reduced low frequency (LF) and high frequency (HF) power of PWV compared to MDD patients without suicidal ideations. Suicidal score was negatively correlated (r=-0.54;p<;0.05) with LF power and positively (r=0.54; p<;0.01) with HF power. No difference in the average PWV was found between the two groups of participants. Reduced variability in pulse wave velocity in MDD patients with suicidal ideation may lead to arterial stiffness and higher risk of future cardiovascular disease.

Enhanced Inter-subject Brain Computer Interface with Associative Sensorimotor Oscillations

Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI). The underlying hypothesis is that optimally associative inter-subject channels can reduce the effects of outliers and, thus, eliminate dissimilar cortical patterns. The proposed approach has been tested on the dataset IVa from BCI competition III, including EEG data acquired from five healthy subjects who were given visual cues to perform 280 trials of MI for the right hand and right foot. Experimental results have shown increased classification accuracy (81.79%) using the WC-based selected 16 channels compared to the one (56.79%) achieved using all the available 118 channels. The associative channels lie mostly around the sensorimotor regions of the brain, reinforced by the previous literature, describing spatial brain dynamics during sensorimotor oscillations. Apparently, the proposed approach paves the way for optimised EEG channel selection that could boost further the efficiency and real-time performance of BCI systems.

Unifying sensorimotor dynamics in multiclass brain computer interface

Unification of spatial brain dynamics in multiclass brain computer interface (BCI) paradigm reduces computational latencies by using lesser number of electrodes from the sensorimotor regions of the brain. We employ reduced number of channels without compromising performance notably. We apply three spatial filtering methods, i.e., Common Spatial Pattern (CSP), Regularized Common Spatial Pattern (RCSP) and Joint Approximate Diagonalization (JAD) as preprocessing. But, we emphasize on selecting specific EEG montages for BCI development. We achieve best 86.7% classification accuracy for subject k3b applying CSP using only 12 channels from sensorimotor regions instead of using 60 channels from the whole brain. Additionally, the average classification accuracies are 64.4% and 61.4% using 60 channels and 12 channels respectively. Also, the average computational latencies are 6.24s and 1.23s in cases of 60 channels and 12 channels respectively.

Wavelet coherence based channel selection for classifying single trial motor imagery

Multi-channel electroencephalography (EEG) recordings require excessive computation and sometimes engender outliers, which make brain computer interface (BCI) systems inefficient. Thus, optimal channel selection becomes a key factor for developing a more comfortable BCI. This study emphasized on a time-frequency (T-F) coherence method, called as Wavelet Coherence (WC), for selecting lesser number of channels. The selected sets of channels were then used to classify two motor imagery (MI) tasks, i.e., right hand (RH) and right foot (RF). The data was collected from publicly available dataset IVa from BCI Competition III. Common spatial pattern (CSP) with and without regularization were applied as preprocessing techniques. While the classification accuracy is 90% using available 118 channels for subject ay, we have achieved higher classification accuracy of 93% using only 24 channels using CSP with regularization. Interestingly, the achieved classification accuracy for subject av is 67% using 4 channels only, that outperform the classification accuracy (i.e., 61%) achieved using 118 channels.

Identifying depressed patients with and without suicidal ideation by finger photo-plethysmography

Major Depressive Disorder (MDD) is a serious mental disorder that if untreated not only affects physical health but also has a high risk of suicide. While the neurophysiological phenomena that contribute to the formation of Suicidal Ideation (SI) are still ill-defined, clear links between MDD and cardiovascular disease have been reported. The aim of this study is to extract suitable features from arterial pulse signals with a view to predicting SI within MDD and control groups. Sixteen unmedicated MDD patients with a history of SI (MDDSI+), sixteen without SI (MDDSI-) and twenty-nine healthy subjects (CONT) were recruited at a psychiatric clinic in the UAE. Depression severity and SI were assessed using the Hamilton Depression Rating Scale and Beck Depression Inventory. Pulse Wave Amplitude (PWA) was calculated as the difference between the peak (Systole) and the valley (Diastole) of the arterial pulse within each cardiac cycle. Then, 2D Tone-Entropy (TE) features were extracted from the Systole, Diastole and PWA time series. The TE features extracted from Diastole were the best markers for predicting MDDSI+. The overall classification accuracies of Classification and Regression Tree (CART) model by using TE features of Systole, Diastole and PWA were 88.52%, 90.2% and 88.52% respectively. When all TE features were combined, accuracy increased up to 93.44% in identifying MDDSI+/MDDSI-/Control groups.

Application of wavelet-based maximum entropy on the mean in channel optimization for BCI

Localizing event-related cortical sources is a key factor while developing a computationally efficient Brain Computer Interface (BCI). This paper proposes a unified application of wavelet-based Maximum Entropy on the Mean (wMEM), as a channel selection method, for classifying two motor imagery (MI) tasks using optimal electroencephalography (EEG) sources. The EEG data, which are collected from publicly available BCI Competition III, are captured from five healthy individuals. This source optimization tool has been validated with a generic BCI framework, which utilizes common spatial pattern with and without regularization as preprocessing tools. However, the best classification accuracy attained is 98% using only 11 selected channels that is close to 100% attained using available 118 channels. This result summarizes how optimal EEG channels can be used to develop a BCI system without compromising the performance significantly.

Prototyping Arduino and Android based m-health solution for diabetes mellitus patient

Diabetes mellitus is a chronic disease and its prolonged existence may cause proliferation of diverse abnormalities in human physiological system. Maintaining a healthy lifestyle can improve the condition of a diabetic patient. However, continuous monitoring of diabetes level is necessary for adapting diets and others for healthy life, which requires clinical settings or medical consultation. Thus, it becomes impractical for diabetic individuals to sustain glucose level under control all the time. This paper describes a unified prototype of m-health solution for diabetes mellitus community that is based on Arduino and Android. At the user end, a Bluetooth connected Glucometer is used to measure the glucose level, which transfer corresponding information to mobile application (App). The App is connected to a central server that facilitates access to medical services or expert consultation. Mass use of this solution after clinical trials will not only enhance the living of diabetic patients, but also will save time.