Detection of breast cancer in digital breast tomosynthesis with vision transformers.

Digital Breast Tomosynthesis (DBT) has revolutionized more traditional breast imaging through its three-dimensional (3D) visualization capability that significantly enhances lesion discernibility, reduces tissue overlap, and improves diagnostic precision as compared to conventional two-dimensional (2D) mammography. In this study, we propose an advanced Computer-Aided Detection (CAD) system that harnesses the power of vision transformers to augment DBT's diagnostic efficiency. This scheme uses a neural network to glean attributes from the 2D slices of DBT followed by post-processing that considers features from neighboring slices to categorize the entire 3D scan.

The Impact of Speaker Diarization on DNN-based Autism Severity Estimation.

This paper presents a speech-based system for autism severity estimation combined with automatic speaker diarization. Speaker diarization was performed by two different methods. The first used acoustic features, which included Mel-Frequency Cepstral Coefficients (MFCC) and pitch, and the second used x-vectors - embeddings extracted from Deep Neural Networks (DNN).

Cough detection using a non-contact microphone: A nocturnal cough study.

An automatic non-contact cough detector designed especially for night audio recordings that can distinguish coughs from snores and other sounds is presented. Two different classifiers were implemented and tested: a Gaussian Mixture Model (GMM) and a Deep Neural Network (DNN). The detected coughs were analyzed and compared in different sleep stages and in terms of severity of Obstructive Sleep Apnea (OSA), along with age, Body Mass Index (BMI), and gender.

Audio source separation to reduce sleeping partner sounds: a simulation study.

When recording a subject in an at-home environment for sleep evaluation or for other breathing disorder diagnoses using non-contact microphones, the breathing recordings (audio signals) can be distorted by sounds such as TV, outside noise, or air-conditioners. If two people are sleeping together, both may produce breathing/snoring sounds that need to be separated. In this study, we present signal processing and source separation algorithms for the enhancement of individual breathing/snoring audio signals in a simulated environment.

Automatic Audio-Based Classification of Patient Inhaler Use: A Pharmacy Based Study.

Chronic respiratory diseases may be controlled through the delivery of medication to the airways and lungs using an inhaler. However, adherence to correct inhaler technique is poor, which impedes patients from receiving maximum clinical benefit from their medication. In this study, the Inhaler Compliance Assessment device was employed to record audio of patients using a Diskus dry powder inhaler.

Speaker diarization during noisy clinical diagnoses of autism.

Autism Spectrum Disorder (ASD) is characterized by difficulties in social communication, social interactions and repetitive behaviors. Some of these difficulties are apparent in the speech characteristics of ASD children who are verbal. Developing algorithms that can extract and quantify speech features that are unique to ASD children is, therefore, extremely valuable for assessing the initial state of each child and their development over time.

Sleep staging using nocturnal sound analysis.

Sleep staging is essential for evaluating sleep and its disorders. Most sleep studies today incorporate contact sensors that may interfere with natural sleep and may bias results. Moreover, the availability of sleep studies is limited, and many people with sleep disorders remain undiagnosed.

A new computer-aided detection approach based on analysis of local and global mammographic feature asymmetry.

Purpose: This study aims to develop and test a new computer-aided detection (CAD) approach and scheme, assessing the likelihood of a subject harboring breast abnormalities.

Methods: The proposed scheme is based on the analysis of both local and global bilateral mammographic feature asymmetries. The level of local or global asymmetry is assessed by analyzing mammographic features extracted from the bilaterally matched regions of interest (ROIs), or from the entire breast, respectively.

Objective Assessment of Patient Inhaler User Technique Using an Audio-Based Classification Approach.

Many patients make critical user technique errors when using pressurised metered dose inhalers (pMDIs) which reduce the clinical efficacy of respiratory medication. Such critical errors include poor actuation coordination (poor timing of medication release during inhalation) and inhaling too fast (peak inspiratory flow rate over 90 L/min). Here, we present a novel audio-based method that objectively assesses patient pMDI user technique.

Spatial acoustic radiation of respiratory sounds for sleep evaluation.

Body posture has an effect on sleeping quality and breathing disorders and therefore it is important to be recognized for the completion of the sleep evaluation process. Since humans have a directional acoustic radiation pattern, it is hypothesized that microphone arrays can be used to recognize different body postures, which is highly practical for sleep evaluation applications that already measure respiratory sounds using distant microphones. Furthermore, body posture may have an effect on distant microphone measurement; hence, the measurement can be compensated if the body posture is correctly recognized.

Advances in Audio-Based Systems to Monitor Patient Adherence and Inhaler Drug Delivery.

Hundreds of millions of people worldwide have asthma and COPD. Current medications to control these chronic respiratory diseases can be administered using inhaler devices, such as the pressurized metered dose inhaler and the dry powder inhaler. Provided that they are used as prescribed, inhalers can improve patient clinical outcomes and quality of life.

A new computer-aided detection scheme based on assessment of local bilateral mammographic feature asymmetry - a preliminary evaluation.

Accurate segmentation of breast lesions depicting on two-dimensional projection mammograms has been proven very difficult and unreliable. In this study we investigated a new approach of a computer-aided detection (CAD) scheme of mammograms without lesion segmentation. Our scheme was developed based on the detection and analysis of region-of-interest (ROI)-based bilateral mammographic tissue or feature asymmetry.

Breathing and Snoring Sound Characteristics during Sleep in Adults.

Study Objectives: Sound level meter is the gold standard approach for snoring evaluation. Using this approach, it was established that snoring intensity (in dB) is higher for men and is associated with increased apnea-hypopnea index (AHI). In this study, we performed a systematic analysis of breathing and snoring sound characteristics using an algorithm designed to detect and analyze breathing and snoring sounds.

Diurnal and seasonal variation of cough episodes in healthy young adults.

Setting: We developed an algorithm to assess recorded cough episodes and differentiate them from similar, non-cough sounds.

Objective: To measure cough episodes in healthy young adults, cigarette smokers and non-smokers over a 24-hour recording period, during the course of normal activity.

Design: The study subjects were students, aged 20-40 years old.

Supraventricular Tachycardia Classification in the 12-Lead ECG Using Atrial Waves Detection and a Clinically Based Tree Scheme.

Specific supraventricular tachycardia (SVT) classification using surface ECG is considered a challenging task, since the atrial electrical activity (AEA) waves, which are a crucial element for obtaining diagnosis, are frequently hidden. In this paper, we present a fully automated SVT classification method that embeds our recently developed hidden AEA detector in a clinically based tree scheme. The process begins with initial noise removal and QRS detection.

Sleep-wake evaluation from whole-night non-contact audio recordings of breathing sounds.

Study Objectives: To develop and validate a novel non-contact system for whole-night sleep evaluation using breathing sounds analysis (BSA).

Design: Whole-night breathing sounds (using ambient microphone) and polysomnography (PSG) were simultaneously collected at a sleep laboratory (mean recording time 7.1 hours).

Atrial electrical activity detection using linear combination of 12-lead ECG signals.

ECG analysis is the method for cardiac arrhythmia diagnosis. During the diagnostic process many features should be taken into consideration, such as regularity and atrial activity. Since in some arrhythmias, the atrial electrical activity (AEA) waves are hidden in other waves, and a precise classification from surface ECG is inapplicable, a confirmation diagnosis is usually performed during an invasive procedure.

Automatic detection of whole night snoring events using non-contact microphone.

Objective: Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology.

Design: Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed.

Obstructive apnea hypopnea index estimation by analysis of nocturnal snoring signals in adults.

Study Objective: To develop a whole-night snore sounds analysis algorithm enabling estimation of obstructive apnea hypopnea index (AHI(EST)) among adult subjects.

Design: Snore sounds were recorded using a directional condenser microphone placed 1 m above the bed. Acoustic features exploring intra-(mel- cepstability, pitch density) and inter-(running variance, apnea phase ratio, inter-event silence) snore properties were extracted and integrated to assess AHI(EST).

Automatic detection of obstructive sleep apnea using speech signals.

Obstructive sleep apnea (OSA) is a common disorder associated with anatomical abnormalities of the upper airways that affects 5% of the population. Acoustic parameters may be influenced by the vocal tract structure and soft tissue properties. We hypothesize that speech signal properties of OSA patients will be different than those of control subjects not having OSA.

Nocturnal sound analysis for the diagnosis of obstructive sleep apnea.

A novel method for screening obstructive sleep apnea syndrome (OSAs) based on nocturnal acoustic signal is proposed. Full-night audio signals from sixty subjects were segmented into snore, noise and silence events using semi-automatic algorithm based on Gaussian mixture models which achieves more than 90% (92%) sensitivity (specificity) and produces an average of 2,000 snores per subject. A classification into 3 groups is proposed for the diagnosis: comparison group - non-OSA subjects (apnea hypopnea index, AHI < 10), mild to moderate OSA (10 < AHI < 30) and severe OSA (AHI>30).

Room volume classification from room impulse response using statistical pattern recognition and feature selection.

Classification of the room volume from the room impulse response (RIR) can be useful in acoustic scene analysis applications, using RIR that is provided directly, or estimated from audio recordings. Current methods for estimating the room volume from the RIR require the source-to-receiver distance, and may be sensitive to differences in absorption. A room volume classification method is presented that does not require the source-to-receiver distance, and which is potentially robust to differences in absorption.

A method for automatic fall detection of elderly people using floor vibrations and sound--proof of concept on human mimicking doll falls.

Falls are a major risk for the elderly people living independently. Rapid detection of fall events can reduce the rate of mortality and raise the chances to survive the event and return to independent living. In the last two decades, several technological solutions for detection of falls were published, but most of them suffer from critical limitations.

Fall detection of elderly through floor vibrations and sound.

Falls are very prevalent among the elderly especially in their home. The statistics show that approximately one in every three adults 65 years old or older falls each year. Almost 30% of those falls result in serious injuries.