Nonlinear signal processing, spectral, and fractal based stridor auscultation: A machine learning approach

The work reported in the paper analyses the adventitious stridor breath sound (ST) and the normal bronchial breath sound (BR) using spectral, fractal, and nonlinear signal processing methods. The sixty breath sound signals are subjected to power spectral density (PSD) and wavelet analyses to understand the temporal evolution of the frequency components. The energy envelope of the PSD plot of ST shows three peaks labelled as A (256 Hz), B (369 Hz), and C (540 Hz), of which A alone is present in BR at 265 Hz. The appearance of B and C in the PSD plot of ST is due to the obstructions in the trachea and upper airways caused by lesions. The phase portrait analysis of the time series data of ST and BR gives information about the randomness and the sample entropy of the dynamical system. The study reveals that the fractal dimension and sample entropy values are higher for BR, which may be due to the musical ordered behaviour of ST. The machine learning techniques based on the features extracted from the PSD data and phase portrait parameters offer good predictability, besides the classification of BR and ST, and thereby revealing its potential in pulmonary auscultation.

The Breathing Body, Whistling Flute, and Sonic Divine: Oneness and Distinction in Bengal Vaishnavism’s Devotional Aesthetics

This paper studies complex narratives connecting the Hindu deity Krishna, his melodious flute, and the porous, sonic human body in the popular devotional sect, Bengal Vaishnavism. From the devotee–lover responding to Krishna’s flute call outside, envying the flute’s privileged position on Krishna’s lips, to becoming the deity’s flute through yogic breath–sound fusions—texts abound with nuanced relations of equivalence and differentiation among the devotee–flute–god. Based primarily on readings of Hindu religious texts, and fieldwork in Bengal among makers/players of the bamboo flute, the paper analyses theological constructions correlating body–flute–divinity. Lying at the confluence of yogic, tantric, and devotional thought, the striking conceptual problem about the flute in Bengal Vaishnavism is: are the body, flute and divinity distinct or the same? I argue that the flute’s descriptions in both classical Sanskrit texts and popular oral lore and performances draw together ostensibly opposed religious paradigms of Yoga (oneness with divinity) and passionate devotion/bhakti (difference): its fine, airy feeling fusing with the body’s inner breathing self, and sweet melody producing a subservient temperament towards the lover–god outside. Flute sounds embody the peculiar dialectic of difference-and-identity among devotee–flute–god, much like the flute–lip-lock itself, bringing to affective life the Bengal Vaishnava philosophical foundation of achintya-bhed-abhed (inconceivability between principles of separation and indistinction).

Capabilities of computer analysis of breath sounds in patients with COVID-19

Objective. To develop methods for a rapid distance computer diagnosis of COVID-19 based on the analysis of breath sounds. It is known that changes in breath sounds can be the indicators of respiratory organs diseases. Computer analysis of these sounds can indicate their typical changes caused by COVID-19, and can be used for a rapid preliminary diagnosis of this disease. Materials and methods. The method of fast Fourier transform (FFT) was used for computer analysis of breath sounds, recorded near the mouth of 14 COVID-19 patients (aged 1880 years) and 17 healthy volunteers (aged 548 years). The frequency of breath sound records ranged from 44 to 96 kHz. Unlike the conventional methods of computer analysis for diagnosis of diseases based on respiratory sound studying, we offer to test a high-frequency part of FFT (20006000 kHz). Results. While comparing the breath sound FFT in patients and healthy volunteers, we developed the methods for COVID-19 computer diagnosis and determined the numerical criteria in patients and healthy persons. These criteria do not depend on sex and age of the examined persons. Conclusions. The offered computer methods based on the analysis of breath sound FFT in patients and volunteers permit to diagnose COVID -19 with relatively high diagnostic parameters. These methods can be used in development of noninvasive means for preliminary self-express diagnosis of COVID-19.

Visuality, Sonicity and Corporeality in Installation Art: A Conversation with Jun Nguyen-Hatsushiba

This conversation paper examines the visual, sonic and corporeal entanglements that inform the work of the Vietnamese-American-Japanese artist Jun Nguyen-Hatsushiba. It explores the corporeal and aural qualities that are central to an understanding and sensorial experience of the artist's installations and visual practice. In paying attention to breath, sound and motion in visual art production, Jun Nguyen-Hatsushiba's works reveal how corporeality and sonicity can dismantle the ocular-centrism of visual art. The discussions between Jun and Prarthana map the varied traumatic histories of racial colonialism, war and forced migration that haunt Vietnam's present, and bring to the surface the artist's aesthetic and political concerns around art, performance and cultural memory.

Influence of observer preferences and auscultatory skill on the choice of terms to describe lung sounds: a survey of staff physicians, residents and medical students

BackgroundIn contrast with the technical progress of the stethoscope, lung sound terminology has remained confused, weakening the usefulness of auscultation. We examined how observer preferences regarding terminology and auscultatory skill influenced the choice of terms used to describe lung sounds.MethodsThirty-one staff physicians (SP), 65 residents (R) and 47 medical students (MS) spontaneously described the audio recordings of 5 lung sounds classified acoustically as: (1) normal breath sound; (2) wheezes; (3) crackles; (4) stridor and (5) pleural friction rub. A rating was considered correct if a correct term or synonym was used to describe it (term use ascribed to preference). The use of any incorrect terms was ascribed to deficient auscultatory skill.ResultsRates of correct sound identification were: (i) normal breath sound: SP=21.4%; R=11.6%; MS=17.1%; (ii) wheezes: SP=82.8%; R=85.2%; MS=86.4%; (iii) crackles: SP=63%; R=68.5%; MS=70.7%; (iv) stridor: SP=92.8%; R=90%; MS=72.1% and (v) pleural friction rub: SP=35.7%; R=6.2%; MS=3.2%. The 3 groups used 66 descriptive terms: 17 were ascribed to preferences regarding terminology, and 49 to deficient auscultatory skill. Three-group agreement on use of a term occurred on 107 occasions: 70 involved correct terms (65.4%) and 37 (34.6%) incorrect ones. Rate of use of recommended terms, rather than accepted synonyms, was 100% for the wheezes and the stridor, 55% for the normal breath sound, 22% for the crackles and 14% for the pleural friction rub.ConclusionsThe observers’ ability to describe lung sounds was high for the wheezes and the stridor, fair for the crackles and poor for the normal breath sound and the pleural friction rub. Lack of auscultatory skill largely surpassed observer preference as a factor determining the choice of terminology. Wide dissemination of educational programs on lung auscultation (eg, self-learning via computer-assisted learning tools) is urgently needed to promote use of standardised lung sound terminology.