Electrical and Electronic Engineering - Research Publications
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ItemNovel Measures of Similarity and Asymmetry in Upper Limb Activities for Identifying Hemiparetic Severity in Stroke Survivors(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-06)Stroke survivors are often characterized by hemiparesis, i.e., paralysis in one half of the body, severely affecting upper limb movements. Monitoring the progression of hemiparesis requires manual observation of limb movements at regular intervals, and hence is a labour intensive process. In this work, we use wrist-worn accelerometers for automated assessment of hemiparesis in acute stroke. We propose novel measures of similarity and asymmetry in hand activities through bivariate Poincaré analysis between two-hand accelerometer data for quantifying hemiparetic severity. The proposed descriptors characterize the distribution of activity surrogates derived from acceleration of the two hands, on a 2D bivariate Poincaré Plot. Experiments show that while the descriptors CSD1 and CSD2 can identify hemiparetic patients from control subjects, their normalized difference CSDR and the descriptors Complex Cross-Correlation Measure ( C3M) and Activity Asymmetry Index ( AAI) can distinguish between mild, moderate and severe hemiparesis. These measures are compared with traditional measures of cross-correlation and evaluated against the National Institutes of Health Stroke Scale (NIHSS), the clinical gold standard for hemiparetic severity estimation. This study, undertaken on 40 acute stroke patients with varying levels of hemiparesis and 15 healthy controls, validates the use of short length ( 5 minutes) wearable accelerometry data for identifying hemiparesis with greater clinical sensitivity. Results show that the proposed descriptors with a hierarchical classification model outperform state-of-the-art methods with overall accuracy of 0.78 and 0.85 for 4-class and 3-class hemiparesis identification respectively.
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ItemAutomatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016-07)Epilepsy is one of the most common neurological disorders and patients suffer from unprovoked seizures. In contrast, psychogenic nonepileptic seizures (PNES) are another class of seizures that are involuntary events not caused by abnormal electrical discharges but are a manifestation of psychological distress. The similarity of these two types of seizures poses diagnostic challenges that often leads in delayed diagnosis of PNES. Further, the diagnosis of PNES involves high-cost hospital admission and monitoring using video-electroencephalogram machines. A wearable device that can monitor the patient in natural setting is a desired solution for diagnosis of convulsive PNES. A wearable device with an accelerometer sensor is proposed as a new solution in the detection and diagnosis of PNES. The seizure detection algorithm and PNES classification algorithm are developed. The developed algorithms are tested on data collected from convulsive epileptic patients. A very high seizure detection rate is achieved with 100% sensitivity and few false alarms. A leave-one-out error of 6.67% is achieved in PNES classification, demonstrating the usefulness of wearable device in the diagnosis of PNES.
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ItemAutomated Scoring of Hemiparesis in Acute Stroke From Measures of Upper Limb Co-Ordination Using Wearable Accelerometry.(Institute of Electrical and Electronics Engineers, 2020-04)Stroke survivors usually experience paralysis in one half of the body, i.e., hemiparesis, and the upper limbs are severely affected. Continuous monitoring of hemiparesis progression hours after the stroke attack involves manual observation of upper limb movements by medical experts in the hospital. Hence it is resource and time intensive, in addition to being prone to human errors and inter-rater variability. Wearable devices have found significance in automated continuous monitoring of neurological disorders like stroke. In this paper, we use accelerometer signals acquired using wrist-worn devices to analyze upper limb movements and identify hemiparesis in acute stroke patients, while they perform a set of proposed spontaneous and instructed movements. We propose novel measures of time (and frequency) domain coherence between accelerometer data from two arms at different lags (and frequency bands). These measures correlate well with the clinical gold standard of measurement of hemiparetic severity in stroke, the National Institutes of Health Stroke Scale (NIHSS). The study, undertaken on 32 acute stroke patients with varying levels of hemiparesis and 15 healthy controls, validates the use of short length (< 10 minutes) accelerometry data to identify hemiparesis through leave-one-subject-out cross-validation based hierarchical discriminant analysis. The results indicate that the proposed approach can distinguish between controls, moderate and severe hemiparesis with an average accuracy of 91%.
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ItemUpper limb movement profiles during spontaneous motion in acute stroke(IOP Publishing, 2021-05-11)Objective: The clinical assessment of upper limb hemiparesis in acute stroke involves repeated manual examination of hand movements during instructed tasks. This process is labour-intensive and prone to human error as well as being strenuous for the patient. Wearable motion sensors can automate the process by measuring characteristics of hand activity. Existing work in this direction either uses multiple sensors or complex instructed movements, or analyzes only the quantity of upper limb motion. These methods are obtrusive and strenuous for acute stroke patients and are also sensitive to noise. In this work, we propose to use only two wrist-worn accelerometer sensors to study the quality of completely spontaneous upper limb motion and investigate correlation with clinical scores for acute stroke care. Approach: The velocity time series estimated from acquired acceleration data during spontaneous motion is decomposed into smaller movement elements. Measures of density, duration and smoothness of these component elements are extracted and their disparity is studied across the two hands. Main results: Spontaneous upper limb motion in acute stroke can be decomposed into movement elements that resemble point-to-point reaching tasks. These elements are smoother and sparser in the normal hand than in the hemiparetic hand, and the amount of smoothness correlates with hemiparetic severity. Features characterizing the disparity of these movement elements between the two hands show statistical significance in differentiating mild-to-moderate and severe hemiparesis. Using data from 67 acute stroke patients, the proposed method can classify the two levels of hemiparetic severity with 85% accuracy. Additionally, compared to activity-based features, the proposed method is robust to the presence of noise in acquired data. Significance: This work demonstrates that the quality of upper limb motion can characterize and identify hemiparesis in stroke survivors. This is clinically significant towards the continuous automated assessment of hemiparesis in acute stroke using minimally intrusive wearable sensors.
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ItemNovel features for capturing temporal variations of rhythmic limb movement to distinguish convulsive epileptic and psychogenic nonepileptic seizures(WILEY, 2019-01)OBJECTIVE: To investigate the characteristics of motor manifestation during convulsive epileptic and psychogenic nonepileptic seizures (PNES), captured using a wrist-worn accelerometer (ACM) device. The main goal was to find quantitative ACM features that can differentiate between convulsive epileptic and convulsive PNES. METHODS: In this study, motor data were recorded using wrist-worn ACM-based devices. A total of 83 clinical events were recorded: 39 generalized tonic-clonic seizures (GTCS) from 12 patients with epilepsy, and 44 convulsive PNES from 7 patients (one patient had both GTCS and PNES). The temporal variations in the ACM traces corresponding to 39 GTCS and 44 convulsive PNES events were extracted using Poincaré maps. Two new indices-tonic index (TI) and dispersion decay index (DDI)-were used to quantify the Poincaré-derived temporal variations for every GTCS and convulsive PNES event. RESULTS: The TI and DDI of Poincaré-derived temporal variations for GTCS events were higher in comparison to convulsive PNES events (P < 0.001). The onset and the subsiding patterns captured by TI and DDI differentiated between epileptic and convulsive nonepileptic seizures. An automated classifier built using TI and DDI of Poincaré-derived temporal variations could correctly differentiate 42 (sensitivity: 95.45%) of 44 convulsive PNES events and 37 (specificity: 94.87%) of 39 GTCS events. A blinded review of the Poincaré-derived temporal variations in GTCS and convulsive PNES by epileptologists differentiated 26 (sensitivity: 70.27%) of 44 PNES events and 33 (specificity: 86.84%) of 39 GTCS events correctly. SIGNIFICANCE: In addition to quantifying the motor manifestation mechanism of GTCS and convulsive PNES, the proposed approach also has diagnostic significance. The new ACM features incorporate clinical characteristics of GTCS and PNES, thus providing an accurate, low-cost, and practical alternative to differential diagnosis of PNES.
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ItemA pilot study of high frequency accelerometry-based sedation and agitation monitoring in critically ill patients(College of Intensive Care Medicine of Australia and New Zealand, 2020-09)Objective: The degree of sedation or agitation in critically ill patients is typically assessed with the Richmond Agitation and Sedation Scale (RASS). However, this approach is intermittent and subject to unrecognised variation between assessments. High frequency accelerometry may assist in achieving a quantitative and continuous assessment of sedation while heralding imminent agitation. Design: We undertook a prospective, observational pilot study. Setting: An adult tertiary intensive care unit in Melbourne, Australia. Participants: 20 patients with an admission diagnosis of trauma. Main outcome measures: Accelerometers were applied to patients' wrists and used to continuously record patient movement. Video data of patient behaviour were simultaneously collected, and observers blinded to accelerometry data were adjudicated the RASS score every 30 seconds. Exploratory analyses were undertaken. Results: Patients were enrolled for a median duration of 9.7 hours (interquartile range [IQR], 0-22.8) and a total of 160 hours. These patients had a median RASS score of 0 (IQR, -4 to 0). A 2-minute moving window of amplitude variance was seen to reflect contemporaneous fluctuations in motor activity and was proportional to the RASS score. Furthermore, the moving window of amplitude variance was observed to spike immediately before ≥ 2 point increases in the RASS score. Conclusions: We describe a novel approach to the analysis of wrist accelerometry data in critically ill patients. This technique not only appears to provide novel and continuous information about the depth of sedation or degree of agitation, it is also notable in its aptitude to anticipate impending transitions to higher RASS values. DESIGN: We undertook a prospective, observational pilot study. SETTING: An adult tertiary intensive care unit in Melbourne, Australia. PARTICIPANTS: 20 patients with an admission diagnosis of trauma. MAIN OUTCOME MEASURES: Accelerometers were applied to patients' wrists and used to continuously record patient movement. Video data of patient behaviour were simultaneously collected, and observers blinded to accelerometry data were adjudicated the RASS score every 30 seconds. Exploratory analyses were undertaken. RESULTS: Patients were enrolled for a median duration of 9.7 hours (interquartile range [IQR], 0-22.8) and a total of 160 hours. These patients had a median RASS score of 0 (IQR, -4 to 0). A 2-minute moving window of amplitude variance was seen to reflect contemporaneous fluctuations in motor activity and was proportional to the RASS score. Furthermore, the moving window of amplitude variance was observed to spike immediately before ≥ 2 point increases in the RASS score. CONCLUSIONS: We describe a novel approach to the analysis of wrist accelerometry data in critically ill patients. This technique not only appears to provide novel and continuous information about the depth of sedation or degree of agitation, it is also notable in its aptitude to anticipate impending transitions to higher RASS values.