Beyond Convergence: Identifiability of Machine Learning and Deep Learning Models

Machine learning (ML) and deep learning models are extensively used for parameter optimization and regression problems. However, not all inverse problems in ML are ``identifiable,'' indicating that model parameters may not be uniquely determined from the available data and the data model's input-output relationship. In this study, we investigate the notion of model parameter identifiability through a case study focused on parameter estimation from motion sensor data. Utilizing a bipedal-spring mass human walk dynamics model, we generate synthetic data representing diverse gait patterns and conditions. Employing a deep neural network, we attempt to estimate subject-wise parameters, including mass, stiffness, and equilibrium leg length. The results show that while certain parameters can be identified from the observation data, others remain unidentifiable, highlighting that unidentifiability is an intrinsic limitation of the experimental setup, necessitating a change in data collection and experimental scenarios. Beyond this specific case study, the concept of identifiability has broader implications in ML and deep learning. Addressing unidentifiability requires proven identifiable models (with theoretical support), multimodal data fusion techniques, and advancements in model-based machine learning. Understanding and resolving unidentifiability challenges will lead to more reliable and accurate applications across diverse domains, transcending mere model convergence and enhancing the reliability of machine learning models

Processing Polysomnographic Signals, using Independent Component Analysis

International audienceIn this paper several applications of the Independent Component Analysis (ICA) algorithm, for the analysis of biomedical signal recordings have been investigated. One of these applications is the removal of EEG artifacts such as the EOG. It is shown that ICA may serve as a powerful tool, which could help the analysis of biomedical recordings, and give better insights about the underlying sources of some disorders. Another application of the proposed method is the detection of sleep disorders in patients suffering from sleep apnea. The ultimate goal of this approach is to develop an automatic noninvasive data acquisition system, for clinical applications

A Synthetic Electrocardiogram (ECG) Image Generation Toolbox to Facilitate Deep Learning-Based Scanned ECG Digitization

The electrocardiogram (ECG) is an accurate and widely available tool for diagnosing cardiovascular diseases. ECGs have been recorded in printed formats for decades and their digitization holds great potential for training machine learning (ML) models in algorithmic ECG diagnosis. Physical ECG archives are at risk of deterioration and scanning printed ECGs alone is insufficient, as ML models require ECG time-series data. Therefore, the digitization and conversion of paper ECG archives into time-series data is of utmost importance. Deep learning models for image processing show promise in this regard. However, the scarcity of ECG archives with reference time-series is a challenge. Data augmentation techniques utilizing \textit{digital twins} present a potential solution. We introduce a novel method for generating synthetic ECG images on standard paper-like ECG backgrounds with realistic artifacts. Distortions including handwritten text artifacts, wrinkles, creases and perspective transforms are applied to the generated images, without personally identifiable information. As a use case, we generated an ECG image dataset of 21,801 records from the 12-lead PhysioNet PTB-XL ECG time-series dataset. A deep ECG image digitization model was built and trained on the synthetic dataset, and was employed to convert the synthetic images to time-series data for evaluation. The signal-to-noise ratio (SNR) was calculated to assess the image digitization quality vs the ground truth ECG time-series. The results show an average signal recovery SNR of 27$\pm$2.8\,dB, demonstrating the significance of the proposed synthetic ECG image dataset for training deep learning models. The codebase is available as an open-access toolbox for ECG research

Impact of coadministration of apigenin and bone marrow stromal cells on damaged ovaries due to chemotherapy in rat: An experimental study

Background: Apigenin is a plant-derived flavonoid with antioxidative and antiapoptotic effects. Bone marrow stromal cells (BMSCs) are a type of mesenchymal stem cells (MSCs) that may recover damaged ovaries. It seems that apigenin may promote the differentiation of MSCs. Objective: The aim of this study was to investigate the effect of coadministration of apigenin and BMSCs on the function, structure, and apoptosis of the damaged ovaries after creating a chemotherapy model with cyclophosphamide in rat. Materials and Methods: For chemotherapy induction and ovary destruction, cyclophosphamide was injected intraperitoneally to 40 female Wistar rats (weighing 180–200 gr, 10 wk old) for 14 days. Then, the rats were randomly divided into four groups (n = 10/each): control, apigenin, BMSCs and coadministration of apigenin and BMSCs. Injection of apigenin was performed intraperitoneally and BMSC transplantation was performed locally in the ovaries. The level of anti-mullerian hormone serum by ELISA kit, the number of oocytes by superovulation, the number of ovarian follicles in different stages by H&E staining, and the expression of ovarian Bcl-2 and Bax proteins by western blot were assessed after four wk. Results: The results of serum anti-mullerian hormone level, number of oocytes and follicles, and Bcl-2/Bax expression ratio showed that coadministration of apigenin and BMSCs significantly recovered the ovarian function, structure, and apoptosis compared to the control, BMSC, and apigenin groups (p < 0.001). Conclusion: The results suggest that the effect of coadministration of apigenin and BMSCs is maybe more effective than the effect of their administrations individually on the recovery of damaged ovaries following the chemotherapy with cyclophosphamide in rats. Key words: Apigenin, Bone marrow stromal cells, Chemotherapy, Ovary, Regeneration

A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias

Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices has been driven predominantly by the increased prevalence of hypertension and its associated risks and clinical conditions. Recent guidelines advocate for regular BP monitoring as part of regular clinical visits or even at home. This increased utilization of BP measurement technologies has brought up significant concerns, regarding the accuracy of reported BP values across settings. In this survey, focusing mainly on cuff-based BP monitoring technologies, we highlight how BP measurements can demonstrate substantial biases and variances due to factors such as measurement and device errors, demographics, and body habitus. With these inherent biases, the development of a new generation of cuff-based BP devices which use artificial-intelligence (AI) has significant potential. We present future avenues where AI-assisted technologies can leverage the extensive clinical literature on BP-related studies together with the large collections of BP records available in electronic health records. These resources can be combined with machine learning approaches, including deep learning and Bayesian inference, to remove BP measurement biases and to provide individualized BP-related cardiovascular risk indexes

Electrode Selection for Noninvasive Fetal Electrocardiogram Extraction using Mutual Information Criteria

International audienceBlind source separation (BSS) techniques have revealed to be promising approaches for the noninvasive extraction of fetal cardiac signals from maternal abdominal recordings. From previous studies, it is now believed that a carefully selected array of electrodes well-placed over the abdomen of a pregnant woman contains the required 'information' for BSS, to extract the complete fetal components. Based on this idea, previous works have involved array recording systems and sensor selection strategies based on the Mutual Information (MI) criterion. In this paper the previous works have been extended, by considering the 3-dimensional aspects of the cardiac electrical activity. The proposed method has been tested on simulated and real maternal abdominal recordings. The results show that the new sensor selection strategy together with the MI criterion, can be effectively used to select the channels containing the most 'information' concerning the fetal ECG components from an array of 72 recordings. The method is hence believed to be useful for the selection of the most informative channels in online applications, considering the different fetal positions and movements

Sequential blind source extraction for quasi-periodic signals with time-varying period

A novel second-order-statistics-based sequential blind extraction algorithm for blind extraction of quasi-periodic signals, with time-varying period, is introduced in this paper. Source extraction is performed by sequentially converging to a solution that effectively diagonalizes autocorrelation matrices at lags corresponding to the time-varying period, which thereby explicitly exploits a key statistical nonstationary characteristic of the desired source. The algorithm is shown to have fast convergence and yields significant improvement in signal-to-interference ratio as compared to when the algorithm assumes a fixed period. The algorithm is further evaluated on the problem of separation of a heart sound signal from real-world lung sound recordings. Separation results confirm the utility of the introduced approach, and listening tests are employed to further corroborate the results

Pulmonary Manifestations of SARS Co V 2 Infection in Mild/Severe Patients

The coronavirus disease 2019 (COVID-19) caused viral pneumonia in Wuhan City in China in December of 2019. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily targets the lungs with severe hypoxia, which usually results in death. COVID-19 is highly heterogeneous regarding severity, clinical phenotype, and more importantly, global dispersal. The respiratory system in all aspects such as respiratory airways, endothelium of pulmonary vessels, conducting airways, the alveoli, neuromuscular breathing structure, and pulmonary circulation are affected by this virus. A comprehensive concept of the source and dynamic action of the SARS-CoV-2 and the possible causes of heterogeneity in COVID-19 is required for predicting and managing the illness in acute and chronic stages of the pulmonary sign