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

마르코프 랜덤 필드 모형을 이용한 2개 집단의 혼합 그래프 모형 추정 및 적용

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 협동과정 생물정보학전공, 2022. 8. 원성호.Background Large datasets with a huge number of variables or subjects, such as multi-omics data, have been widely generated recently. Many of these datasets are mixed type including both numeric and categorical variables, which makes their analyses difficult. In some studies, the networks underlying the large dataset may be of interest. There have been several methods that are suggested for the inference of the networks, but most of them can be used only for a single type of data or single class cases. Objective The objective of the study is to develop and propose a new method, named fused MGM (FMGM), that infers network structures underlying mixed data in 2 groups, with assumptions that both the networks and the differences are sparse. Also, statistical analyses including the proposed method were conducted to find biological markers of the atopic dermatitis (AD) and underlying network structures from multi-omics data of 6-month-old infants. Methods For FMGM, the statistical models of the networks are based on pairwise Markov random field model, and the penalty functions implement the main assumption that the networks in 2 groups and their differences are sparse. Fast proximal gradient method (PGM) was used for the optimization of the target function. The extension of FMGM that allows the inclusion of prior knowledges, named prior-induced FMGM (piFMGM), was also developed. The performance of the method was measured with synthetic datasets that simulate power-law network structures. Also, the multi-omics profiles of 6-month-old infants were analyzed. The profiles include host gene transcriptome (N=199), intestinal microbial compositions (N=197), and predicted intestinal microbial functions (N=98; 84 in common). For the analysis, differential analysis with limma and network inference with FMGM were applied. Results From the analysis of simulated 2-class datasets, generated from simulated scale-free networks, FMGM showed superior performances especially in terms of F1-scores compared to the previous method inferring the networks one by one (0.392 & 0.546). FMGM performed better not only in inferring the differences (0.217 & 0.410), but also in inferring the networks (0.492 & 0.572). Utilizing prior information with piFMGM obtained slightly better F1-scores from the inference of networks (0.572 & 0.589), and from the inference of the difference (0.410 & 0.423). As a result, the overall performance showed slight improvement (0.546 & 0.562). From the inference of networks from 6-month-old infants’ AD data, 10 pairs of variables were shown to have different correlations by disease statuses, including host expression of LINC01036 and MIR4788 and abundance of microbial genes related to carotenoid biosynthesis and RNA degradation. Conclusions The proposed method, FMGM inferred the network structures in 2 classes better than the previous method. Inclusion of prior information in piFMGM may be useful in more accurate inference of networks, but since the change was subtle, additional studies may be conducted to improve it. Network inference revealed several markers of AD such as microbial genes related to carotenoid biosynthesis and RNA degradation, suggesting a number of possible underlying metabolisms related to AD such as oxidative stress and microbial RNA balance.연구 배경 최근 다중 오믹스 자료와 같이 다수의 변수 혹은 관찰을 포함하는 대용량 자료가 광범위하게 생산되고 있다. 이러한 자료는 연속형 및 이산형 변수를 모두 포함하는 혼합형 자료인 경우가 많으며, 이는 자료의 통계적 분석을 어렵게 한다. 특히 기저 네트워크 추론의 경우, 그간 몇몇 통계적 방법들이 제시되어 왔으나, 대부분 변수 유형이 단일하거나 집단이 하나인 경우에 대해서만 적용 가능하다. 연구 목적 본 연구에서는 2개 집단의 혼합형 자료로부터 기저 네트워크를 추론하는 방법인 fused MGM (FMGM)을 개발하고 제시하고자 하였다. 이 방법은 네트워크 자체에 더하여 그 차이 역시 전체 자료에 비해 희박한 밀도를 가짐을 가정한다. 또한, 6개월 아동의 다중 오믹스 자료에 이 방법을 포함한 통계적 분석 방법을 적용하여, 아토피성 피부염과 관련된 생물학적 마커 및 기저 네트워크 구조를 찾아내고자 하였다. 연구 방법 FMGM은 쌍별 마르코프 랜덤 필드에 기반한 통계적 모형을 사용하며, 벌점 함수를 통해 네트워크 및 차이의 희박함을 유도한다. 목적함수의 최적화에는 고속 근위 경사법을 사용하였다. 또한 FMGM의 추론에 사전 정보를 도입할 수 있도록 하는 사전 정보 유도 FMGM (piFMGM) 역시 개발하였다. 추론 방법의 성능은 역법칙 네트워크 구조를 시뮬레이션한 합성 자료를 통해 측정하였다. 6개월 아동의 다중 오믹스 정보 역시 분석하였으며, 오믹스 정보에는 숙주 유전자 전사체 (N=199), 장내 미생물체 구성 (N=197) 및 장내 미생물 기능 정보 (N=98)가 포함된다 (공통 표본 수 84). 분석에는 선형 모형을 통한 차이 분석과 FMGM을 통한 네트워크 추론을 사용하였다. 연구 결과 시뮬레이션한 무척도 네트워크로부터 2개 집단 자료를 생성하여 분석한 결과, 개별 집단에 대해 네트워크를 추론한 결과와 비교하여 FMGM이 더 높은 F1 점수를 나타내어 성능이 더 우수함을 보였다 (0.392 & 0.546). FMGM은 네트워크 간 차이 (0.217 & 0.410)뿐만 아니라 네트워크 자체의 추론에서도 더 우수한 성능을 보였다 (0.492 & 0.572). 사전 정보를 piFMGM을 통해 도입한 경우 전체적인 성능이 미세한 증가를 보였다 (0.546 & 0.562). 네트워크의 추론뿐만 아니라 (0.572 & 0.589), 차이를 추론할 때의 성능 역시 작은 증가세를 띄었다 (0.410 & 0.423). 6개월 아동의 아토피성 피부염 자료로부터 네트워크 추론을 수행한 결과 숙주의 LINC01036 및 MIR4788 발현, 장내 미생물의 카로티노이드 생합성 및 RNA 분해 관련 유전자 등, 10개 변수 쌍이 피부염 여부에 따른 상관성 차이를 나타냈다. 결론 본 연구에서 제시한 방법인 FMGM은 기존 방법에 비해 2개 집단의 혼합형 자료에서 네트워크를 추론할 때 더 좋은 성능을 나타냈다. 사전 정보를 piFMGM을 통해 포함시킬 경우 네트워크 추론의 정확성이 향상되나, 그 차이가 크지 않아 추후 연구에서 이를 발전시키기 위한 방법이 필요할 것으로 보인다. 다중 오믹스 자료의 네트워크 추론 분석을 통해 장내 미생물의 카로티노이드 생합성 또는 RNA 분해 관련 유전자 등 아토피성 피부염과 관련된 생물학적 마커를 복수 발견하였으며, 이는 아토피성 피부염의 기저에 산화 스트레스 또는 미생물 RNA 조절 등이 관련될 수 있음을 제시한다.Chapter 1. Introduction 1 1.1 Study Background 1 1.2 Prior Works 2 1.3 Purpose of Research 5 Chapter 2. Network Inference of 2-class Mixed Data 6 2.1 Introduction 6 2.2 Notations 8 2.3 Model Formulation 8 2.4 Optimization with Fast Proximal Gradient Method 12 2.5 Code Implementation 20 2.6 Simulated Data Analysis 20 2.7 Real Data Analysis: DNA Methylation Data 23 2.8 Discussion 26 Chapter 3. Integration of Prior Information for Network Inference 28 3.1 Introduction 29 3.2 Use of Separate Parameter for Prior Information 29 3.3 Determination of Regularization Parameters 30 3.4 Simulated Data Analysis 33 3.5 Real Data Analysis: Multi-Omics Data from Asthma Patients 35 3.6 Discussion 38 Chapter 4. Multi-Omics Data Analysis of Atopic Dermatitis (AD) 39 4.1 Background 39 4.2 Data Description 40 4.3 Statistical Analysis 43 4.4 Results 43 4.5 Discussion 45 Chapter 5. Conclusion 47 Appendix 49 Bibliography 53 Abstract in Korean 59박

Enhancing models and measurements of traffic-related air pollutants for health studies using dispersion modeling and Bayesian data fusion

Research Report 202 describes a study led by Dr. Stuart Batterman at the University of Michigan, Ann Arbor and colleagues. The investigators evaluated the ability to predict traffic-related air pollution using a variety of methods and models, including a line source air pollution dispersion model and sophisticated spatiotemporal Bayesian data fusion methods. Exposure assessment for traffic-related air pollution is challenging because the pollutants are a complex mixture and vary greatly over space and time. Because extensive direct monitoring is difficult and expensive, a number of modeling approaches have been developed, but each model has its own limitations and errors. Dr. Batterman and colleagues sought to improve model estimations by applying and systematically comparing the performance of different statistical models. The study made extensive use of data collected in the Near-road EXposures and effects of Urban air pollutants Study (NEXUS), a cohort study designed to examine the relationship between near-roadway pollutant exposures and respiratory outcomes in children with asthma who live close to major roadways in Detroit, Michigan

Advances in spatiotemporal models for non-communicable disease surveillance

Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public

Environmental risk assessment in the mediterranean region using artificial neural networks

Los mapas auto-organizados han demostrado ser una herramienta apropiada para la clasificación y visualización de grupos de datos complejos. Redes neuronales, como los mapas auto-organizados (SOM) o las redes difusas ARTMAP (FAM), se utilizan en este estudio para evaluar el impacto medioambiental acumulativo en diferentes medios (aguas subterráneas, aire y salud humana). Los SOMs también se utilizan para generar mapas de concentraciones de contaminantes en aguas subterráneas simulando las técnicas geostadísticas de interpolación como kriging y cokriging. Para evaluar la confiabilidad de las metodologías desarrolladas en esta tesis, se utilizan procedimientos de referencia como puntos de comparación: la metodología DRASTIC para el estudio de vulnerabilidad en aguas subterráneas y el método de interpolación espacio-temporal conocido como Bayesian Maximum Entropy (BME) para el análisis de calidad del aire. Esta tesis contribuye a demostrar las capacidades de las redes neuronales en el desarrollo de nuevas metodologías y modelos que explícitamente permiten evaluar las dimensiones temporales y espaciales de riesgos acumulativos