Breathing Signature as Vitality Score Index Created by Exercises of Qigong: Implications of Artificial Intelligence Tools Used in Traditional Chinese Medicine.

Rising concerns about the short- and long-term detrimental consequences of administration of conventional pharmacopeia are fueling the search for alternative, complementary, personalized, and comprehensive approaches to human healthcare. Qigong, a form of Traditional Chinese Medicine, represents a viable alternative approach. Here, we started with the practical, philosophical, and psychological background of Ki (in Japanese) or Qi (in Chinese) and their relationship to Qigong theory and clinical application. Noting the drawbacks of the current state of Qigong clinic, herein we propose that to manage the unique aspects of the Eastern 'non-linearity' and 'holistic' approach, it needs to be integrated with the Western "linearity" "one-direction" approach. This is done through developing the concepts of "Qigong breathing signatures," which can define our life breathing patterns associated with diseases using machine learning technology. We predict that this can be achieved by establishing an artificial intelligence (AI)-Medicine training camp of databases, which will integrate Qigong-like breathing patterns with different pathologies unique to individuals. Such an integrated connection will allow the AI-Medicine algorithm to identify breathing patterns and guide medical intervention. This unique view of potentially connecting Eastern Medicine and Western Technology can further add a novel insight to our current understanding of both Western and Eastern medicine, thereby establishing a vitality score index (VSI) that can predict the outcomes of lifestyle behaviors and medical conditions

Towards the Design of a Smartphone-Based Biofeedback Breathing Training: Indentifying Diaphragmatic Breathing Patterns From a Smartphones\u27 Microphone

Asthma, diabetes, hypertension, or major depression are non-communicable diseases (NCDs) and impose a major burden on global health. Stress is linked to both the causes and consequences of NCDs and it has been shown that biofeedback-based breathing trainings (BBTs) are effective in coping with stress. Here, diaphragmatic breathing, i.e. deep abdominal breathing, belongs to the most distinguished breathing techniques. However, high costs and low scalability of state-of-the-art BBTs that require expensive medical hardware and health professionals, represent a significant barrier for their widespread adoption. Health information technology has the potential to address this important practical problem. Particularly, it has been shown that a smartphone microphone has the ability to record audio signals from exhalation in a quality that can be compared to professional respiratory devices. As this finding is highly relevant for low-cost and scalable smartphone-based BBTs (SBBT) and – to the best of our knowledge - because it has not been investigated so far, we aim to design and evaluate the efficacy of such a SBBT. As a very first step, we apply design-science research and investigate in this research-in-progress the relationship of diaphragmatic breathing and its acoustic components by just using a smartphone’s microphone. For that purpose, we review related work and develop our hypotheses based on justificatory knowledge from physiology, physics and acoustics. We finally describe a laboratory study that is used to test our hypotheses. We conclude with a brief outlook on future work

Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study

Background: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved. Objective: The objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex. Methods: We used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex. Results: We recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean –0.1 (95% CI –12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI –3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch–based sex classification performed best yielding an accuracy of 83%. Conclusions: Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma

SPARC 2018 Internationalisation and collaboration : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2018 SPARC conference. This year we not only celebrate the work of our PGRs but also the launch of our Doctoral School, which makes this year’s conference extra special. Once again we have received a tremendous contribution from our postgraduate research community; with over 100 presenters, the conference truly showcases a vibrant PGR community at Salford. These abstracts provide a taster of the research strengths of their works, and provide delegates with a reference point for networking and initiating critical debate. With such wide-ranging topics being showcased, we encourage you to take up this great opportunity to engage with researchers working in different subject areas from your own. To meet global challenges, high impact research inevitably requires interdisciplinary collaboration. This is recognised by all major research funders. Therefore engaging with the work of others and forging collaborations across subject areas is an essential skill for the next generation of researchers