Maziyar Baran Pouyan

In-bed posture classification and limb identification

We propose an algorithm that uses pressure image data to detect a persons sleeping posture and identifies different body limbs. Our algorithm can be used in monitoring bed-bound patients and assessing the risk of pressure ulceration. We used a GMM-based clustering approach for concurrent posture classification and limb identification. Our proposed technique, applied on 9 healthy subjects instructed to sleep in 13 different postures, resulted in 98.4% classification accuracy in distinguishing three main stable sleeping postures. Additionally, 8 limbs in supine and 5 limbs in left/right side postures were identified with the overall accuracy of 91.6%.

Automated seizure detection using limited-channel EEG and non-linear dimension reduction

Electroencephalography (EEG) is an essential component in evaluation of epilepsy. However, full-channel EEG signals recorded from 18 to 23 electrodes on the scalp is neither wearable nor computationally effective. This paper presents advantages of both channel selection and nonlinear dimension reduction for accurate automatic seizure detection. We first extract the frequency domain features from the full-channel EEG signals. Then, we use a random forest algorithm to determine which channels contribute the most in discriminating seizure from non-seizure events. Next, we apply a non-linear dimension reduction technique to capture the relationship among data elements and map them in low dimension. Finally, we apply a KNN classifier technique to discriminate between seizure and non-seizure events. The experimental results for 23 patients show that our proposed approach outperforms other techniques in terms of accuracy. It also visualizes long-term data in 2D to enhance physician cognition of occurrence and disease progression.

Unsupervised EEG analysis for automated epileptic seizure detection

Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patients history.

Automatic limb identification and sleeping parameters assessment for pressure ulcer prevention

Pressure ulcers (PUs) are common among vulnerable patients such as elderly, bedridden and diabetic. PUs are very painful for patients and costly for hospitals and nursing homes. Assessment of sleeping parameters on at-risk limbs is critical for ulcer prevention. An effective assessment depends on automatic identification and tracking of at-risk limbs. An accurate limb identification can be used to analyze the pressure distribution and assess risk for each limb. In this paper, we propose a graph-based clustering approach to extract the body limbs from the pressure data collected by a commercial pressure map system. A robust signature-based technique is employed to automatically label each limb. Finally, an assessment technique is applied to evaluate the experienced stress by each limb over time. The experimental results indicate high performance and more than 94% average accuracy of the proposed approach.

Clustering Single-Cell Expression Data Using Random Forest Graphs

Complex tissues such as brain and bone marrow are made up of multiple cell types. As the study of biological tissue structure progresses, the role of cell-type-specific research becomes increasingly important. Novel sequencing technology such as single-cell cytometry provides researchers access to valuable biological data. Applying machine-learning techniques to these high-throughput datasets provides deep insights into the cellular landscape of the tissue where those cells are a part of. In this paper, we propose the use of random-forest-based single-cell profiling, a new machine-learning-based technique, to profile different cell types of intricate tissues using single-cell cytometry data. Our technique utilizes random forests to capture cell marker dependences and model the cellular populations using the cell network concept. This cellular network helps us discover what cell types are in the tissue. Our experimental results on public-domain datasets indicate promising performance and accuracy of our technique in extracting cell populations of complex tissues.

Single and multi-subject clustering of flow cytometry data for cell-type identification and anomaly detection

BackgroundMeasurement of various markers of single cells using flow cytometry has several biological applications. These applications include improving our understanding of behavior of cellular systems, identifying rare cell populations and personalized medication. A common critical issue in the existing methods is identification of the number of cellular populations which heavily affects the accuracy of results. Furthermore, anomaly detection is crucial in flow cytometry experiments. In this work, we propose a two-stage clustering technique for cell type identification in single subject flow cytometry data and extend it for anomaly detection among multiple subjects.ResultsOur experimentation on 42 flow cytometry datasets indicates high performance and accurate clustering (F-measure > 91 %) in identifying main cellular populations. Furthermore, our anomaly detection technique evaluated on Acute Myeloid Leukemia dataset results in only <2 % false positives.

Gaits analysis using pressure image for subject identification

In this paper, a method for human identification using footprints obtained by a pressure sensor pad is proposed. The proposed method registers the sequence of pressure images in time and spatial domain and uses principle component analysis to produce a compact feature vector. The method is tested on footprints collected from 35 subjects and an accuracy of 97% is observed based on a 10-fold cross validation using SVM classifier.

A Non-EEG Biosignals Dataset for Assessment and Visualization of Neurological Status

Neurological assessment can be used to monitor a persons neurological status. In this paper, we report collection and analysis of a multimodal dataset of Non-EEG physiological signals available in the public domain. We have found this signal set useful for inferring the neurological status of individuals. The data was collected using non-invasive wrist worn biosensors and consists of electrodermal activity (EDA), temperature, acceleration, heart rate (HR), and arterial oxygen level (SpO2). We applied an efficient non-linear dimension reduction technique to visualize the biosignals in a low dimension feature space. We could cluster the four neurological statuses using an unsupervised Gaussian Mixture Model. The experimental results show that our unsupervised method can accurately separate different neurological statuses with an accuracy of greater than 84%.

Identifying Cell Populations in Flow Cytometry Data Using Phenotypic Signatures

Single-cell flow cytometry is a technology that measures the expression of several cellular markers simultaneously for a large number of cells. Identification of homogeneous cell populations, currently done by manual biaxial gating, is highly subjective and time consuming. To overcome the shortcomings of manual gating, automatic algorithms have been proposed. However, the performance of these methods highly depends on the shape of populations and the dimension of the data. In this paper, we have developed a time-efficient method that accurately identifies cellular populations. This is done based on a novel technique that estimates the initial number of clusters in high dimension and identifies the final clusters by merging clusters using their phenotypic signatures in low dimension. The proposed method is called SigClust. We have applied SigClust to four public datasets and compared it with five well known methods in the field. The results are promising and indicate higher performance and accuracy compared to similar approaches reported in literature.

Virtual Sleep Laboratory for Population Health

Sleep apnea, a potentially serious sleep disorder affects more than 18 million Americans according to the National Sleep Foundation. The symptoms are chronic snoring and day time sleeping. Since people with sleep apnea are sleep deprived, they may suffer from various conditions such as difficulty in concentration, depression, irritability, heart arrhythmia, high blood pressure, sexual dysfunction, learning and memory difficulties and falling sleep at work or while driving. The cost of Sleep Apnea to the national economy due to loss of life and productivity is estimated to be multiple billions of dollars per year.