threats. These CRDs are diseases of the airways and other structures of the lung. Some of the most common CRDs are chronic obstructive pulmonary disease (COPD), asthma, occupational lung diseases and pulmonary hypertension. Each year, 235 million people suffer from asthma which is a common disease among children [3]. More than 90% of COPD deaths are occurring in LMICs [3], and more than 3 million people die from COPD which is estimated to be 6% of all deaths worldwide [3].
Early life environment has been shown to influence the risk of developing a wide range of non- communicable diseases (NCDs) in the later life [22, 23, 31]. For example, environmental factors, such as, diet and level of physical activities are likely to play a major role in the development of NCDs [22]. Furthermore, several studies have reported that environmental pollution has an impact on morbidity and mortality in children aged 0–5, not just in high income countries, but also in low- and middle-income countries [27,30]. In particular, Latzinet. al. have provided the evidence of the association between the air pollution exposure during pregnancy and the reduced postnatal lung function [28]. Korten et. al. recently showed a sustained effect of prenatal air pollution exposure on lung function at age 5 weeks up to the age of 11 years [29]. In several epidemiological studies, maternal nutrition in pregnancy has been clearly observed to have associations with offspring risk of cardiovascular, metabolic and repository NCDs in later life [24, 25].
Growing evidences found in the literature clearly indicate that the early life environment can play an important role in influencing the risk of developing a wide range of NCDs in the later life [22]. The epidemiologic observations like smaller size or relative thinness at birth and dur- ing infancy are associated with increased rates of coronary heart disease, stroke, type 2 diabetes mellitus, adiposity, the metabolic syndrome, and osteoporosis in the adult life [63–67]. In par- ticular, maternal exposures during pregnancy are key early-life determinants of offspring illness.
Exposure to environmental pollutants, such as, tobacco smoke or air pollution, in utero has also been associated with adverse neurodevelopmental, respiratory or cardiovascular outcomes [33].
Further, maternal psychosocial factors, including stress and anxiety, are associated with offspring phenotypes, such as, asthma [34], obesity [35], or cardiovascular dysfunction [36].
A key limitation in the above-mentioned research works is that the data used therein come from high-income countries. Zaret. al. and Steinet. al. have been investigating the impact of early life
exposures in an African birth cohort, the Drakenstein Child Health Study (DCHS) [37,38], in a low- middle income (LMIC) setting. In the DCHS, maternal HIV exposure was observed to be associated with poor neurodevelopment in HIV-exposed uninfected (HEU) infants at 2 years of age [59] and faecal bacterial profiles of infants at 4 -12 weeks of age [26]. According to Grithini et. al., HIV- positive children and adolescents have a significant prevalence of lung function impairment, namely, primarily irreversible lower airway obstruction and impaired aerobic function [60]. A subset of the authors in the DCHS reported that HIV exposure was associated with altered lung function in HIV exposed uninfected (HEU) infants at both 6 weeks and 2 years of age, with impairment associated with uncontrolled maternal HIV [39, 48]. However, possible mechanisms linking HIV exposure and these health outcomes are still unclear. Breen et. al. also reported that antenatal maternal psychological distress in the DCHS was associated with a pattern of gene expression signatures in cord blood, with distinct gene expression modules associated with maternal depression and post-traumatic stress disorder (PTSD) [41].
Given these observations, one of the aims of this study is to explore the association between HIV exposure and cord blood gene expression and to investigate whether this is linked to offspring lung function.
2.8.2 Lung function prediction from the voice sound files
Asthma, one of the major non-communicable diseases, is a common respiratory condition. Each year around 235 million people suffer from asthma worldwide [68]. It is the most common chronic disease among children. Over 80% of deaths due to asthma occurs in low- and lower-middle income countries [68]. In Bangladesh it appears to be a substantial public health problem [72, 73].
Khan et al. reported that an estimation of 11.6 million people, including 4.1 million children, suffered from asthma-related symptoms based on a study on two years’ (i.e., 1999 and 2010) data of Bangladesh [73]. According to the WHO data published in 2018, asthma deaths in Bangladesh reached 14,674 or 1.89% of total deaths [69].
The complex nature of asthma means that many different techniques can be used for monitoring, including subjective symptom assessments, lung function testing and measurement of biomarkers.
Regular monitoring of asthma can help patients to receive appropriate treatment in time, which
can help to reduce patient’s symptoms, frequency of exacerbation, and hospitalisation. The ability to monitor asthma and modify treatment appropriately could help to reduce the burden of cost for asthma treatment. Identifying symptoms via questionnaire along with lung function measurement via spirometry and identification of biomarkers (e.g., exhaled nitric oxide or sputum eosinophils) - all can be used in regular monitoring of asthma. These are mostly impractical to implement in community-based care particularly in the context of low-income countries due to expense and/or complexity. Historical objective measures of lung function have been important for long term asthma monitoring. However, special equipment and expertise are required to assess measures of lung function, such as, forced expiratory volume in one second (FEV1).
Self-monitoring of asthma has the potential to play an important role in maintaining disease control. However, such monitoring is required to be simple, convenient, and accurate. Equipment, such as, smart spirometers together with accompanying smartphone apps used to record the peak expiratory flow rates (PEFR), and provide reminders to the users via text message to manage asthma more efficiently, are currently available to simplify self-monitoring [74]. However, smart spirometers are still very expensive for personal use. As 31.0 million people in Bangladesh use smartphones [76], an smartphone application that would measure lung function, store, and alert patients to modify their treatment without the need for a smart spirometer could be a convenient inexpensive way for monitoring asthma.
Speech is commonly used for assessing acute asthma. At present, subjective assessment of the rate of speech, such as, from “speaking full sentences” to “unable to speak at all” and “wheeze”
are recorded in clinician’s notes and together with sounds of breathing, such as, ‘wheezing or whistling in the chest’ are used to assess acute asthma symptoms [77–79]. Although no standardised assessment or quantitative measures of these features have been developed so far, the effects on speech and breathing patterns and sounds due to increased airway resistance are noticeable in acute asthma [80]. Thus, pitch and quality of the breathing sound can be utilized potentially to predict lung function.
In the literature, mostly three kinds of sounds have been analysed in this context. They are as follows: (1) lung sound and breathing sound from chest, (2) cough sound, and (3) voice sound.
Detterbeck et. al. worked on quantitative breath sound measurements (called vibration response
imaging (VRI)) to predict postoperative lung function [81]. Westhoff et. al. used quantitative breath sound testing by VRI to predict values for postoperative lung function [82]. Recently, Haider et. al. used respiratory sound to classify COPD using machine learning [83]. Most of the above studies predicted postoperative lung function.
Pramono et. al. recently developed a cough-based algorithm for automatic diagnosis of per- tussis1 [84]. Subsequently, Raoet. al. proposed a method for automatically predicting spirometer readings from cough and wheeze audio signals for asthma severity monitoring [85]. Windmon et.
al. presented a smart-phone system to identify cough episodes as early symptoms of COPD and congestive heart failure [86]. Botha et. al. proposed a technique to detect tuberculosis by auto- matic cough sound analysis [87]. Sharan et. al. investigated the possibility of using cough sound analysis for the prediction of spirometer measurements and used linear and nonlinear regression models [88]. Porter et. al. presented a diagnostic accuracy study very recently for pediatric res- piratory disease using an automated cough-sound analyser [89]. Subsequently, Rudraraju et. al.
analysed cough sound with a goal to correlate spirometer and clinical diagnosis. All these above studies used cough sounds i.e., symptom based sounds.
In parallel to the research works mentioned above that focused on symptom based sounds (i.e., cough sound), there are a number of works in the literature that worked with voice sounds only. Tayler et. al. performed a clinical assessment and showed that speech correlated well with lung function during induced bronchoconstriction [91]. Kutor et. al. recently worked on speech signal as an alternative to spirometer in asthma diagnosis [92]. The purpose of this research was to investigate the correlation between FEV1/FVC (Forced Expiratory Volume to Forced Vital Capacity) ratio obtained from spirometer and Harmonics-to-Noise Ratio (HNR) obtained from human speech. Very recently, Ashraf et. al. have reported a proprietary software with some preliminary results on voice-based screening and monitoring of chronic respiratory condition in Chest annual meeting [93]. Subsequently, Saleheen et. al. have proposed a convenient mobile- based approach that detect a monosyllabic voice segment called ‘A-vowel’ sound or ‘Aaaa...’ sound from voice to estimate lung function [94]. Chunet. al. have proposed two algorithms very recently for passive assessment of pulmonary condition: one for the detection of obstructive pulmonary
1Pertussis, also known as whooping cough, is a highly contagious respiratory disease.
disease and the other for the estimation of the pulmonary function in terms of FEV1/FVC ratio and FEV1% [95]. There are a few studies in the literature that worked with voice sounds to predict lung function albeit with below par performances. Chun et. al. [95] predicted lung function in terms of FEV1% (i.e., regression). The performance of their model were reported in mean absolute error in percentage (MAE (%)). The MAE (%) has been found as 20.6% which is quite large. In addition to that most of these recent studies presented their approaches and methods without comparing the result with previous studies. On the contrary, in this thesis, we have proposed a new methodology to predict lung function to monitor asthma from the voice sounds. The voices were recorded for 1 min while the subjects read standard texts. Table 2.1 summarizes the related studies in this context.
Table 2.1: Summary of the related studies in this domain
Study Disease Voice
(1 min)
Sound type
Normal vs. Ab- normal LF
Severity of LF
Regression
This study
Asthma Yes Voice Yes Yes Yes
Two main factors: LF prediction in terms of FEV1%, Voice based Larson et.
al. in 2012 [74]
- No Other
(blow)
- - -
They simulated the breathing out blow captured through smartphone and calculated lung function from the blow. They evaluated their model on 52 subjects and reported the mean error of 5.1% compared to a clinical spirometer for common measures of lung function.
Goel et. al.
in 2016 [96]
- No Other
(blow)
- - -
Table 2.1 –Continued from the previous page
Study Disease Voice
(1 min)
Sound type
Normal vs. Ab- normal LF
Severity of LF
Regression
They investigated how spirometry sensing could be performed from any phone which was used the standard telephony voice channel to transmit the sound of the spirome- try effort. They evaluated their models with 50 participants with two gold standard medical spirometers and claimed that the application had an acceptable mean error.
Brashier and Salvi in 2015 [97]
- No Other
(FOT/IOS)
- - -
This study is not based on recorded voice. The authors conducted this study to in- vestigate the role of the forced oscillation technique (FOT) and impulse oscillometry system (IOS) to measure the lung function using sound waves. They measured the lung function instead of predicting it.
Sharan et.
al. in 2018 [88]
COPD No Cough - - -
This study uses cough sounds and the cough sound is different than voice sound.
They collected cough sounds from 322 adults and performed linear and non-linear regression. They reported high positive correlation of COPD diagnosis in predicting FEV1 and FVC.
Pramono et. al. in 2016 [84]
Pertussis No Cough - - -
This study mainly deals with cough sounds to diagnosis pertussis. The authors presented a logistic regression model to diagnosis pertussis and stated that their model automatically detected individual cough sounds with 92% accuracy and PPV of 97%.
Rao et. al.
in 2017 [85]
- No Cough &
Wheeze
- Yes -
Table 2.1 –Continued from the previous page
Study Disease Voice
(1 min)
Sound type
Normal vs. Ab- normal LF
Severity of LF
Regression
This study uses cough sounds. The authors performed experiments on 16 healthy persons and 12 patients and reported results of prediction of FEV1%, FVC% and their ratio achieving an RMSE of 11.06%, 10.3% and 0.08 respectively.
Windmon et. al. in 2018 [86]
COPD & Heart No Cough - - -
This study uses cough sounds. They used classifiers in 2 levels. At the first-level, the classifier identifies whether or not a given cough segment indicates a disease. If yes, the second-level classifier identifies the cough segment as symptomatic of COPD or congestive heart failure (CHF). They performed experiment with a cohort of 9 COPD, 9 CHF, and 18 healthy (control) subjects. To classify the disease vs. the healthy they achieved a Sensitivity of 80%, Specificity of 82% and an Accuracy of 80.67% and the ROC Area of 83%. To identify COPD and CHF at the second level they achieved a Sensitivity of 82%, Specificity of 75% and an Accuracy of 78.05%
and the ROC Area of 80%.
Botha et.
al. in 2018 [87]
Tuberculosis No Cough - - -
This study uses cough sounds. To distinguish the coughs of Tuberculosis positive patients and healthy controls they achieved an accuracy of 78% and an AUC of 0.95.
Porter et.
al. in 2019 [89]
Paediatric Resp.
Disease
No Cough - - -
This study uses cough sounds. They performed experiments on 585 subjects. They reported their results in the Positive Percent and Negative Percent Agreement values between the automated analyser and the clinical reference. These were as follows:
asthma (97, 91%); pneumonia (87, 85%); lower respiratory tract disease (83, 82%);
croup (85, 82%); bronchiolitis (84, 81%).
Table 2.1 –Continued from the previous page
Study Disease Voice
(1 min)
Sound type
Normal vs. Ab- normal LF
Severity of LF
Regression
Rudraraju et. al. in 2020 [90]
- No Cough - Yes -
This study uses cough sounds. They constructed a machine learning model to predict obstructive versus restrictive patterns. They showed a strong correlation of cough sound characteristics with airflow characteristics (including FEV1, FVC and their ratios). They claimed that their pattern prediction accuracy was 91.97%, sensitivity was 87.2%, and specificity was 93.69%.
Laguarta et. al. in 2020 [98]
COVID-19 No Cough - - -
This study uses cough sounds. They conducted experiments on 5,320 subjects and developed a Convolutional Neural Network (CNN). They reported the results of their model for COVID-19 with sensitivity of 98.5% with a specificity of 94.2%
(AUC: 0.97).
Kutor et.
al. in 2019 [92]
Asthma No Voice
(but vowel sounds)
- - -
The authors tried to find the correlation between FEV1/FVC from the spirome- try and Harmonics-to-Noise Ratio (HNR) in sound signals. They used 33 samples and showed the highest correlation coefficient between HNR and vowel sound /:/
(42.08%).
Tayler et.
al. in 2015 [91]
Asthma Yes Voice - - -
Table 2.1 –Continued from the previous page
Study Disease Voice
(1 min)
Sound type
Normal vs. Ab- normal LF
Severity of LF
Regression
This study worked with voice sounds but their focus was different. They used only 7 asthmatic patients. These patients underwent a bronchial challenge and were then recorded reading a standardized text for 1 minutes. A total of 88 healthcare professionals heard the records to estimate FEV1% predicted. These estimations showed moderate correlation to FEV1% (rho=0.61 P¡0.01).
Westhoff et. al. in 2013 [82]
- No Other
(breath sound from chest)
- - -
The authors used quantitative breath sound testing by vibration response imaging (VRI) as well as predicted post-operative (PPO) lung function. They conducted experiments on fifty-three subjects. They mentioned that VRI predictions showed good correlation for the 34 patients with actual PPO lung function (R=0.88 and R=0.80 for FEV(1)% and FEV(1) L, respectively).
Haider et.
al. in 2019 [83]
COPD No Other
(lung sound from chest)
- - -
They used lung sound from chest. They developed the classification models for normal and COPD subjects on the basis of respiratory sound analysis. They reported the maximum classification accuracy of 83.6% achieved by the SVM classifier.
Table 2.1 –Continued from the previous page
Study Disease Voice
(1 min)
Sound type
Normal vs. Ab- normal LF
Severity of LF
Regression
Detterbeck et. al. in 2013 [81]
- No Other
(Breath sound from chest)
- - -
They used breath sound testing by VRI as well as predicted post-operative (PPO) lung function. They performed experiments on 135 patients. They found a good agreement between calculated estimations of PPO lung function using VRI and perfusion measurements (PPO-FEV(1)%: r = 0.95; -8% to 11.5%; PPO-Dlco: r = 0.97; -6.6% to 9.5%).
Chun et.
al. in 2020 [95]
Asthma / COPD Yes Voice Yes (but
healthy vs.
asthma/COPD)
No Yes
They used voice sounds (spontaneous speech and reading) to predict healthy vs.
pathological and lung function prediction in terms of FEV1/FVA & FEV1%. They reported the detection accuracy of the pathological class as 73.7% and the F1 score as 84.5% (87.2% precision and 82.0% recall). Their regression model, using voice sounds of the reading texts in predicting FEV1%, achieved a MAE(%) of 20.6%.
Saleheen et.
al. in Oct 2020 [94]
- No Voice
(but mono- syllabic)
- - Yes
Table 2.1 –Continued from the previous page
Study Disease Voice
(1 min)
Sound type
Normal vs. Ab- normal LF
Severity of LF
Regression
This study uses voice sound but detect monosyllabic (A-vowel’ sound or ‘Aaaa...’
sound) from sound first. And then predict from ‘A-vowel’. They stated that A-vowel sounds could be detected with 93% accuracy, and A-vowel sounds could estimate lung functions with 7.4-11.35% MAE(%).
Ashraf et.
al. in Oct 2020 [93]
Not mentioned (but used PFT)
Not men- tioned clearly
Voice Yes (but
not clear)
No No
This study uses voice sound to predict LF. Most importantly, they presented an interim result and developed a proprietary app. No clear methodology has been described therein in detail.
2.8.3 Disease diagnosis using feature ranking
In recent times, the application of computational or machine intelligence in medical diagnostics has become quite common. Machine intelligence aided decision systems are often being adopted to assist (but not to replace) a physician in diagnosing the disease of a patient. A (human) physician typically accumulates her knowledge based on patients’ symptoms and the confirmed diagnoses.
Thus diagnostic accuracy is highly dependent on a physician’s experience. Since it is now relatively easy to acquire and store a lot of information digitally, the deployment of computerized medical decision support systems has become a viable approach to assisting physicians to swiftly and accurately diagnose patients [117]. Such a system can be seen as a classification task as the goal is to make a prediction (i.e., diagnosis) on a new case based on the available records and features (of
previously known cases). Such classification tasks are considered to be one of the most challenging tasks in disease informatics [118].
While various statistical techniques may be applied in medical data classification, the major drawback of these approaches is that they depend on some assumptions (e.g., related to the prop- erties of the relevant data) for their successful application [119, 120]. To know the properties of the dataset is a hard job and often is infeasible. On the other hand, soft computing based approaches are less dependent on such knowledge.
A number of soft computing based classifiers have been proposed and analyzed in the literature to classify medical data accurately. Abbass et al. proposed a system with pareto-differential evaluation algorithm with local search scheme, called Memetic Pareto-Artificial Neural Network (MPANN) to diagnose breast cancer [121]. Subsequently, Kiyanet al. [122] presented a statistical neural network-based approach to diagnose breast cancer. In [123], Karabatak et al. developed an expert system for detecting breast cancer, where, to reduce the dimensions of the dataset, Association Rules (AR) were used. Peng et al. proposed a hybrid feature selection approach to deal with the issues of high dimensionality of biomedical data and experimented on the breast cancer dataset [124]. Fana et al. combined case-based data clustering and fuzzy decision tree to design a hybrid model for medical data classification [125]. The model was trained and evaluated on two datasets, WBC and liver disorders. Azaret al. proposed three classification methods, namely, radial basis function (RBF), multilayer perceptron (MLP), and probabilistic neural network (PNN) and experimented on a breast cancer dataset [126]. In their experiments, PNN showed better performance than MLP.
In recent years, several works on medical data classification have been reported in the liter- ature, albeit only on breast cancer dataset. Examples include, but may not be limited to, back propagation (BP-NN) approach [127], fuzzy-rough nearest neighbor method [128], PCA followed by Support Vector Machine (SVM) with Recursive Feature Elimination (SVM-RFE) [129], PCA in combination with a feed-forward neural network [130], ANN with MLP and also BP-NN [131], deep belief network (DBN) [132], SVM ensembles with bagging and boosting [133], knowledge- based system using Expectation Maximization (EM) clustering, noise removal, Regression Trees (CART) [134]. Motivated by the promising results of [132], very recently, Karthiket al.[135] have