Diagnosis of Peripheral Artery Disease Using Backflow Abnormalities in Proximal Recordings of Accelerometer Contact Microphone (ACM).

Objective: The development of an accurate, non-invasive method for the diagnosis of peripheral artery disease (PAD) from accelerometer contact microphone (ACM) recordings of the cardiac system.

Methods: Mel frequency cepstral coefficients (MFCCs) are initially extracted from ACM recordings. The extracted MFCCs are then used to fine-tune a pre-trained ResNet50 network whose middle layers provide streams of high-level-of-abstraction coefficients (HLACs) which could provide information on blood pressure backflow caused by arterial obstructions in PAD patients.

Fetal Movement Cancellation in Abdominal Electrocardiogram Recordings Using Signal-to-Signal Translation.

This study addresses the cancellation of fetal movement in abdominal electrocardiogram (AECG) recordings through deep neural networks. For this purpose, a generative signal-to-signal translation model consisting of two coupled generators is employed to discover the relations between fetal movement-contaminated and clean AECG recordings. The model is trained on the fetal ECG synthetic database (FECGSYNDB) which provides AECG recordings from 10 pregnancies along with their ground-truth maternal and fetal ECG signals.

Fetal Electrocardiogram Extraction Using Dual-Path Source Separation of Single-Channel Non-Invasive Abdominal Recordings.

Objective: The development of a method for non-invasive monitoring of fetal electrocardiogram (FECG) signals from single-channel abdominal recordings.

Methods: The dual-path source separation (DPSS) architecture is introduced for the simultaneous separation of fetal and maternal ECG signals from abdominal ECG recordings. DPSS initially denoises abdominal ECG (AECG) recordings using a generative dual-path long short-term memory (DP-LSTM) network.

Real-time high-resolution millimeter-wave imaging for in-vivo skin cancer diagnosis.

High-resolution millimeter-wave imaging (HR-MMWI), with its high discrimination contrast and sufficient penetration depth, can potentially provide affordable tissue diagnostic information noninvasively. In this study, we evaluate the application of a real-time system of HR-MMWI for in-vivo skin cancer diagnosis. 136 benign and malignant skin lesions from 71 patients, including melanoma, basal cell carcinoma, squamous cell carcinoma, actinic keratosis, melanocytic nevi, angiokeratoma, dermatofibroma, solar lentigo, and seborrheic keratosis were measured.

Multi-modal Framework for Fetal Heart Rate Estimation: Fusion of Low-SNR ECG and Inertial Sensors.

This study presents a novel multi-modal framework for fetal heart rate extraction, which incorporates wearable seismo-cardiography (SCG), gyro-cardiography (GCG), and electrocardiography (ECG) readings from ten pregnant women. Firstly, a signal refinement method based on empirical mode decomposition (EMD) is proposed to extract the desired signal components associated with fetal heart rate (FHR). Afterwards, two techniques are developed to fuse the information from different modalities.

An Open-Access Database for the Evaluation of Cardio-Mechanical Signals From Patients With Valvular Heart Diseases.

This paper describes an open-access database for seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. The archive comprises SCG and GCG recordings sourced from and processed at multiple sites worldwide, including Columbia University Medical Center and Stevens Institute of Technology in the United States, as well as Southeast University, Nanjing Medical University, and the first affiliated hospital of Nanjing Medical University in China. It includes electrocardiogram (ECG), SCG, and GCG recordings collected from 100 patients with various conditions of valvular heart diseases such as aortic and mitral stenosis.

Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals.

This paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analysis framework utilizing Elastic Net was implemented to reduce the features generated by continuous wavelet transform (CWT).

A Fetal Movement Simulation System for Wearable Vibrational Sensors.

This paper introduces a low-cost phantom system that simulates fetal movements (FMVs) for the first time. This vibration system can be used for testing wearable inertial sensors which detect FMVs from the abdominal wall. The system consists of a phantom abdomen, a linear stage with a stepper motor, a tactile transducer, and control circuits.

Classification of Aortic Stenosis Before and After Transcatheter Aortic Valve Replacement Using Cardio-mechanical Modalities.

This paper reports our study on the impact of transcatheter aortic valve replacement (TAVR) on the classification of aortic stenosis (AS) patients using cardio-mechanical modalities. Machine learning algorithms such as decision tree, random forest, and neural network were applied to conduct two tasks. Firstly, the pre- and post-TAVR data are evaluated with the classifiers trained in the literature.

A Hybrid Radar-Camera Respiratory Monitoring System Based on an Impulse-Radio Ultrawideband Radar.

This paper reports a pilot study of a hybrid radar-camera system that simultaneously monitors the respiration of two subjects. A prototype system was built involving a low-cost impulse-radio ultra-wideband (IR-UWB) radar module and an optical and depth-sensing camera module. The system detects subjects using the camera and utilizes the distance information acquired to guide the signal processing of the radar.

A Fast Parameter Identification Framework for Personalized Pharmacokinetics.

This paper introduces a novel framework for fast parameter identification of personalized pharmacokinetic problems. Given one sample observation of a new subject, the framework predicts the parameters of the subject based on prior knowledge from a pharmacokinetic database. The feasibility of this framework was demonstrated by developing a new algorithm based on the Cluster Newton method, namely the constrained Cluster Newton method, where the initial points of the parameters are constrained by the database.

Classification of Aortic Stenosis Using Time-Frequency Features From Chest Cardio-Mechanical Signals.

Objectives: This paper introduces a novel method for the detection and classification of aortic stenosis (AS) using the time-frequency features of chest cardio-mechanical signals collected from wearable sensors, namely seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. Such a method could potentially monitor high-risk patients out of the clinic.

Methods: Experimental measurements were collected from twenty patients with AS and twenty healthy subjects.

A Smartphone-Only Pulse Transit Time Monitor Based on Cardio-Mechanical and Photoplethysmography Modalities.

This paper reports a system for monitoring pulse transit time (PTT). Using an Android smartphone and a customized sensing circuit, the system collects seismo-cardiogram (SCG), gyro-cardiogram (GCG), and photoplethysmogram (PPG) recordings. There is no need for any other external stand-alone systems.

High-Contrast, Low-Cost, 3-D Visualization of Skin Cancer Using Ultra-High-Resolution Millimeter-Wave Imaging.

The goal of this paper is to develop a new skin imaging modality which addresses the current clinical need for a non-invasive imaging tool that images the skin over its depth with high resolutions while offering large histopathological-like contrasts between malignant and normal tissues. We demonstrate that by taking advantage of the intrinsic millimeter-wave dielectric contrasts between normal and malignant skin tissues, ultra-high-resolution millimeter-wave imaging (MMWI) can achieve 3-D, high-contrast images of the skin. In this paper, an imaging system with a record-wide bandwidth of 98 GHz is developed using the synthetic ultra-wideband millimeter-wave imaging approach, a new ultra-high-resolution imaging technique recently developed by the authors.

A Binary Classification of Cardiovascular Abnormality Using Time-Frequency Features of Cardio-mechanical Signals.

This paper introduces a novel method of binary classification of cardiovascular abnormality using the time-frequency features of cardio-mechanical signals, namely seismocardiography (SCG) and gyrocardiography (GCG) signals. A digital signal processing framework is proposed which utilizes decision tree and support vector machine methods with features generated by continuous wavelet transform. Experimental measurements were collected from twelve patients with cardiovascular diseases as well as twelve healthy subjects to evaluate the proposed method.

A Low-cost, Smartphone-only Pulse Transit Time Measurement System Using Cardio-mechanical Signals and Optical Sensors.

This paper presents a smartphone-only solution for measuring pulse transit time (PTT). An application based on an Android smartphone is developed to collect seismocardiogram (SCG), gyrocardiogram (GCG), and photoplethysmography (PPG) recordings. The system does not need any other external system for measurements, so the total cost and system complexity are minimized.

An Independent Component Analysis Approach to Motion Noise Cancelation of Cardio-Mechanical Signals.

This paper proposes a new framework for measuring sternal cardio-mechanical signals from moving subjects using multiple sensors. An array of inertial measurement units are attached to the chest wall of subjects to measure the seismocardiogram (SCG) from accelerometers and the gyrocardiogram (GCG) from gyroscopes. A digital signal processing method based on constrained independent component analysis is applied to extract the desired cardio-mechanical signals from the mixture of vibration observations.

Synthetic Ultra-High-Resolution Millimeter-Wave Imaging for Skin Cancer Detection.

This work introduces, for the first time, a millimeter-wave imaging system with a "synthetic" ultra-wide imaging bandwidth of 98 GHz to provide the ultra-high resolutions required for early-stage skin cancer detection. The proposed approach consists of splitting the required ultra-wide imaging bandwidth into four sub-bands, and assigning each sub-band to a separate imaging element, i.e.

Ultrawideband, Stable Normal and Cancer Skin Tissue Phantoms for Millimeter-Wave Skin Cancer Imaging.

This work introduces new, stable, and broadband skin-equivalent semisolid phantoms for mimicking interactions of millimeter waves with the human skin and skin tumors. Realistic skin phantoms serve as an invaluable tool for exploring the feasibility of new technologies and improving design concepts related to millimeter-wave skin cancer detection methods. Normal and malignant skin tissues are separately mimicked by using appropriate mixtures of deionized water, oil, gelatin powder, formaldehyde, TX-150 (a gelling agent, widely referred to as "super stuff"), and detergent.

Combined Seismo- and Gyro-Cardiography: A More Comprehensive Evaluation of Heart-Induced Chest Vibrations.

This paper reports on the combined analysis of seismocardiogram (SCG) and gyrocardiogram (GCG) recordings. An inertial measurement unit (IMU) consisting of a three-axis micro-electromechanical (MEMS) accelerometer and a three-axis MEMS gyroscope is used to record heart-induced mechanical vibrations from the chest wall of the subjects. An electrocardiogram and an impedance cardiogram (ICG) sensor are also used as references for segmenting the cardiac cycles and recording the aortic valve opening and closure (AO and AC) events, respectively.

Ultra-Wideband Millimeter-Wave Dielectric Characteristics of Freshly Excised Normal and Malignant Human Skin Tissues.

Millimeter waves have recently gained attention for the evaluation of skin lesions and the detection of skin tumors. Such evaluations heavily rely on the dielectric contrasts existing between normal and malignant skin tissues at millimeter-wave frequencies. However, current studies on the dielectric properties of normal and diseased skin tissues at these frequencies are limited and inconsistent.

Pulse Transit Time Measurement Using Seismocardiogram, Photoplethysmogram, and Acoustic Recordings: Evaluation and Comparison.

This work proposes a novel method of pulse transit time (PTT) measurement. The proximal arterial location data are collected from seismocardiogram (SCG) recordings by placing a micro-electromechanical accelerometer on the chest wall. The distal arterial location data are recorded using an acoustic sensor placed inside the ear.

Motion noise cancellation in seismocardiogram of ambulant subjects with dual sensors.

This paper presents a dual-sensor method of extracting seismocardiographic (SCG) data from moving adult subjects using chest-worn wireless MEMS accelerometers. A digital signal processing (DSP) system including a normalized least means square (NLMS) adaptive filter is designed and tested in MATLAB. Data results from 10 subjects indicate a detection rate of 98.