Parkinson's Disease (PD) patients frequently transition between the 'ON' state, where medication is effective, and the 'OFF' state, affecting their quality of life. Monitoring these transitions is vital for personalized therapy. We introduced a framework based on Reinforcement Learning (RL) to detect transitions between medication states by learning from continuous movement data. Unlike traditional approaches that typically identify each state based on static data patterns, our approach focuses on understanding the dynamic patterns of change throughout the transitions, providing a more generalizable medication state monitoring method. We integrated a deep Long Short-Term Memory (LSTM) neural network and three one-class unsupervised classifiers to implement an RL-based adaptive classifier. We tested on two PD datasets: Dataset PD1 with 12 subjects (14-minute average recording) and Dataset PD2 with seven subjects (120-minute average recording). Data from wrist and ankle wearables captured transitions during 2 to 4-hour daily activities. The algorithm demonstrated its effectiveness in detecting medication states, achieving an average weighted F1-score of 82.94% when trained and tested on Dataset PD1. It performed well when trained on Dataset PD1 and tested on Dataset PD2, with a weighted F1-score of 76.67%. It surpassed other models, was resilient to severe PD symptoms, and performed well with imbalanced data. Notably, prior work has not addressed the generalizability from one dataset to another, essential for real-world applications with varied sensors. Our innovative framework revolutionizes PD monitoring, setting the stage for advanced therapeutic methods and greatly enhancing the life quality of PD patients.

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that could be used to research one’s hemodynamic response to the world. A popular method to analyze the fNIRS signal is to use the grand averaging method, which collapses oxygenated hemoglobin (HBO) over a predefined time of interest (TOI) window. A drawback of the grand averaging method is that it collapses information about the fNIRS signal (e.g., temporal), thus one is not able to observe the temporal dynamics of the signal. Therefore, we propose to use multidimensional signal analysis (i.e., Tucker decomposition [TD] method) for fNIRS signal analysis, which can compress the signal and reveal the changes in the signal across time and space. We used TD on a three-way tensor with temporal, spatial, and subject modes constructed from an fNIRS dataset collected from infants that observed entities (i.e., human hand and mechanical claw) performing various action sequences (i.e., functional and nonfunctional events). Analysis of variance (ANOVA) was applied to the subject dimension to identify significant differences across conditions. We compared the performance of the grand averaging and TD method. Results from the TD method were able to replicate the results from the grand averaging method and identify additional patterns missed by the grand averaging method. Findings from this study demonstrate the TD method as an alternative fNIRS signal analysis method.

Motor fluctuations are a frequent complication in patients with Parkinson's disease (PD) where the response to medication fluctuates between ON states (medication working) and OFF states (medication has worn off). This paper describes a new data analysis approach that can be used along with two wearable IMU (inertial measurement units) sensors to continuously assess motor fluctuations in PD patients while moving in their natural environment. We hypothesized that joint analysis of the sensor data in its spectral, temporal and sensor domain could generate multilevel features that can be used to detect PD-related patterns successfully as the subject's motor state fluctuates between medication ON and OFF states. For this purpose, we utilized time-frequency (TF) representation and multiway data analysis tools (i.e., tensor decomposition) to decompose the TF representation of the two sensors' data into its multilevel structures, which were next used to extract multilevel features representing the PD symptoms in different medication states. The extracted multilevel features were used in a classification model based on support vector machine to detect medication ON and OFF states. For comparison purposes, we implemented a traditional feature extraction method. We also developed a hierarchical feature extraction method based on the combination of those two methods. The performances of the three methods were evaluated using a dataset of 19 PD subjects with a total duration of 17.54 hours. The multilevel features achieved 8.25% improvement in the accuracy over the traditional features, and the hierarchical features resulted in 10.73% improvement indicating that our approach holds great promise to continuously detect medication states from continuous monitoring of the subjects' movement. Such information can be used by the treating physician to tailor the adjustments to each subject's unique impairment(s), thereby improving therapeutic decision-making and patient outcomes.

Tensor decomposition is a promising solution for analyzing and classifying multidimensional data streams in long-term monitoring of physical systems. However, the key challenge is to continuously perform the expensive tensor decomposition to ensure the effective classification of the time-evolving data as the system evolves over time. This paper explores a novel resource-aware tensor decomposition framework using reinforcement learning (RL). The proposed framework is developed to update a tensor decomposition-based classifier only using the data that is important to the evolving nature of the system. It learns an RL agent to look-ahead and identifies the data that is important to the classifier update and performs the tensor decomposition on those data streams. The proposed resource-aware framework was evaluated using synthetic data and motion data from patients with Parkinson's disease and indicated a significant performance in both classification accuracy and number of tensor decompositions compared to a continuous update approach.

Cardiac simulations play an important role in studies involving understanding and investigating the mechanisms of cardiac arrhythmias. Today, studies of arrhythmogenesis and maintenance are largely being performed by creating simulations of a particular arrhythmia with high accuracy comparable to the results of clinical experiments. Atrial fibrillation (AF), the most common arrhythmia in the United States and many other parts of the world, is one of the major field where simulation and modeling is largely used. AF simulations not only assist in understanding its mechanisms but also help to develop, evaluate and improve the computer algorithms used in electrophysiology (EP) systems for ablation therapies. In this paper, we begin with a brief overeview of some common techniques used in simulations to simulate two major AF mechanisms - spiral waves (or rotors) and point (or focal) sources. We particularly focus on 2D simulations using Nygren et al.s mathematical model of human atrial cell. Then, we elucidate an application of the developed AF simulation to an algorithm designed for localizing AF rotors for improving current AF ablation therapies. Our simulation methods and results, along with the other discussions presented in this paper is aimed to provide engineers and professionals with a working-knowledge of application-specific simulations of spirals and foci.

Time-frequency (TF) representations have been widely used over the past decade to characterize the non-stationary content of signals in the joint time and frequency domain. Although a number of effective TF analysis methods based on wavelet or Gabor transform have been developed, these methods use pre-determined basis functions and still require feature extraction methods to reduce redundancy and preserve important TF information related to the application of interest. This paper explores a novel TF feature extraction algorithm using a modified dictionary learning approach. The proposed algorithm is developed to modify learned dictionaries and derive TF features unique to each class. It emulates the way joint dictionary learning algorithms use common dictionaries to promote discrimination between the data from different classes, thereby allowing for an improved analysis of complex and multitask data. The proposed method indicated a significant performance in identification of the discriminant vs. common structures of the TF data.

Audio feature extraction and classification are important tools for audio signal analysis in many applications, such as multimedia indexing and retrieval, and auditory scene analysis. However, due to the nonstationarities and discontinuities exist in these signals, their quantification and classification remains a formidable challenge. In this paper, we develop a new approach for audio feature extraction to effectively quantify these nonstationarities in an attempt to achieve high classification accuracy for environmental audio signals. Our approach consists of three stages: first we propose to construct the time-frequency matrix (TFM) of audio signals using matching-pursuit time-frequency distribution (MP-TFD) technique, and then apply the non-negative matrix decomposition (NMF) technique to decompose the TFM into its significant components. Finally, we propose seven novel features from the spectral and temporal structures of the decomposed vectors in a way that they successfully represent joint TF structure of the audio signal, and combine them with the Mel-frequency cepstral coefficients (MFCCs) features. These features are examined using a database of 192 environmental audio signals which includes 20 aircraft, 17 helicopter, 20 drum, 15 flute, 20 piano, 20 animal, 20 bird, and 20 insect sounds, and the speech of 20 males and 20 females. The results of the numerical simulation support the effectiveness of the proposed approach for environmental audio classification with over 10% accuracy-rate improvement compared to the MFCC features.

Time-frequency matrix (TFM) decomposition using non-negative matrix factorization (NMF) has been recently considered as a successful tool for time-frequency (TF) quantification. In this paper, we modify the constraints of traditional cost function of NMF to make the method a better fit for TF quantification, and denote the new method with NMF discriminant base (NMFDB) decomposition. We evaluate the proposed method, and show that it successfully identifies the discriminant bases. Additionally, we measure the discrimination ability of NMFDB over the signals with very low discriminations, and compare it with the discrimination of the decomposed bases derived using traditional NMF. It is concluded that the proposed method is able to locate the region of difference with 20% better performance compared to the conventional NMF.

In this study, we propose a new way to detect the image watermark messages modulated as linear chirp signals. The spread spectrum image watermarking algorithm embeds linear chirps as watermark messages. The phase of the chirp represents watermark message such that each phase corresponds to a different message. We extract the watermark message using a phase detection algorithm based on discrete polynomial phase transform (DPT). The DPT models the signal as polynomial and uses ambiguity function to estimate the signal parameters. The proposed method not only detects the presence of watermark, but also extracts the embedded watermark bits and ensures the message is received correctly. The robustness of the proposed detection scheme has been evaluated using checkmark benchmark attacks, and we found a guaranteed maximum bit error rate of 15%, which watermark message is correctly detected using DPT