Vinayak Swarnkar

Wavelet Augmented Cough Analysis for Rapid Childhood Pneumonia Diagnosis

Pneumonia is the cause of death for over a million children each year around the world, largely in resource poor regions such as sub-Saharan Africa and remote Asia. One of the biggest challenges faced by pneumonia endemic countries is the absence of a field deployable diagnostic tool that is rapid, low-cost and accurate. In this paper, we address this issue and propose a method to screen pneumonia based on the mathematical analysis of cough sounds. In particular, we propose a novel cough feature inspired by wavelet-based crackle detection work in lung sound analysis. These features are then combined with other mathematical features to develop an automated machine classifier, which can separate pneumonia from a range of other respiratory diseases. Both cough and crackles are symptoms of pneumonia, but their existence alone is not a specific enough marker of the disease. In this paper, we hypothesize that the mathematical analysis of cough sounds allows us to diagnose pneumonia with sufficient sensitivity and specificity. Using a bedside microphone, we collected 815 cough sounds from 91 patients with respiratory illnesses such as pneumonia, asthma, and bronchitis. We extracted wavelet features from cough sounds and combined them with other features such as Mel Cepstral coefficients and non-Gaussianity index. We then trained a logistic regression classifier to separate pneumonia from other diseases. As the reference standard, we used the diagnosis by physicians aided with laboratory and radiological results as deemed necessary for a clinical decision. The methods proposed in this paper achieved a sensitivity and specificity of 94% and 63%, respectively, in separating pneumonia patients from non-pneumonia patients based on wavelet features alone. Combining the wavelets with features from our previous work improves the performance further to 94% and 88% sensitivity and specificity. The performance far surpasses that of the WHO criteria currently in common use in resource-limited settings.

Cough Sound Analysis Can Rapidly Diagnose Childhood Pneumonia

Pneumonia annually kills over 1,800,000 children throughout the world. The vast majority of these deaths occur in resource poor regions such as the sub-Saharan Africa and remote Asia. Prompt diagnosis and proper treatment are essential to prevent these unnecessary deaths. The reliable diagnosis of childhood pneumonia in remote regions is fraught with difficulties arising from the lack of field-deployable imaging and laboratory facilities as well as the scarcity of trained community healthcare workers. In this paper, we present a pioneering class of technology addressing both of these problems. Our approach is centred on the automated analysis of cough and respiratory sounds, collected via microphones that do not require physical contact with subjects. Cough is a cardinal symptom of pneumonia but the current clinical routines used in remote settings do not make use of coughs beyond noting its existence as a screening-in criterion. We hypothesized that cough carries vital information to diagnose pneumonia, and developed mathematical features and a pattern classifier system suited for the task. We collected cough sounds from 91 patients suspected of acute respiratory illness such as pneumonia, bronchiolitis and asthma. Non-contact microphones kept by the patient’s bedside were used for data acquisition. We extracted features such as non-Gaussianity and Mel Cepstra from cough sounds and used them to train a Logistic Regression classifier. We used the clinical diagnosis provided by the paediatric respiratory clinician as the gold standard to train and validate our classifier. The methods proposed in this paper could separate pneumonia from other diseases at a sensitivity and specificity of 94 and 75% respectively, based on parameters extracted from cough sounds alone. The inclusion of other simple measurements such as the presence of fever further increased the performance. These results show that cough sounds indeed carry critical information on the lower respiratory tract, and can be used to diagnose pneumonia. The performance of our method is far superior to those of existing WHO clinical algorithms for resource-poor regions. To the best of our knowledge, this is the first attempt in the world to diagnose pneumonia in humans using cough sound analysis. Our method has the potential to revolutionize the management of childhood pneumonia in remote regions of the world.

Interhemispheric Asynchrony Correlates With Severity of Respiratory Disturbance Index in Patients With Sleep Apnea

Obstructive sleep apnea (OSA) hypopnea syndrome is a disorder characterized by airway obstructions during sleep; full obstructions are known as apnea and partial obstructions are called hypopnea. Sleep in OSA patients is significantly disturbed with frequent apnea/hypopnea and arousal events. We illustrate that these events lead to functional asymmetry of the brain as manifested by the interhemispheric asynchrony (IHA) computed using EEG recorded on the scalp. In this paper, based on the higher order spectra of IHA time series, we propose a new index [interhemispheric synchrony index (IHSI)], for characterizing brain asynchrony in OSA. The IHSI computation does not depend on subjective criteria and can be completely automated. The proposed method was evaluated on overnight EEG data from a clinical database of 36 subjects referred to a hospital sleep laboratory. Our results indicated that the IHSI could classify the patients into OSA/non-OSA classes with an accuracy of 91% (ρ = 0.0001), at the respiratory disturbance index threshold of 10, suggesting that the brain asynchrony carries vital information on OSA.

High frequency analysis of cough sounds in pediatric patients with respiratory diseases

Cough is a common symptom in a range of respiratory diseases and is considered a natural defense mechanism of the body. Despite its critical importance in the diagnosis of illness, there are no golden methods to objectively assess cough. In a typical consultation session, a physician may briefly listen to the cough sounds using a stethoscope placed against the chest. The physician may also listen to spontaneous cough sounds via naked ears, as they naturally propagate through air. Cough sounds carry vital information on the state of the respiratory system but the field of cough analysis in clinical medicine is in its infancy. All existing cough analysis approaches are severely handicapped by the limitations of the human hearing range and simplified analysis techniques. In this paper, we address these problems, and explore the use of frequencies covering a range well beyond the human perception (up to 90 kHz) and use wavelet analysis to extract diagnostically important information from coughs. Our data set comes from a pediatric respiratory ward in Indonesia, from subjects diagnosed with asthma, pneumonia and rhinopharyngitis. We analyzed over 90 cough samples from 4 patients and explored if high frequencies carried useful information in separating these disease groups. Multiple regression analysis resulted in coefficients of determination (R2) of 77-82% at high frequencies (15 kHz-90 kHz) indicating that they carry useful information. When the high frequencies were combined with frequencies below 15kHz, the R2 performance increased to 85-90%.

Sleep-Stage and Event Dependency of Brain Asynchrony as manifested through surface EEG

Electroencephalography (EEG) is a core measurement in overnight sleep studies. In this paper we study functional asymmetries of the brain as manifested through spectral correlation coefficient. Our target group is patients symptomatic of sleep apnea and referred for routine Polysomnography (PSG) testing at the hospital. We measured EEG data (using electrodes C4/A1 and C3/A2 of the International 10/20 System) as a part of the routine PSG test. Spectral correlation coefficients were computed between EEG data from the two hemispheres, for each frequency band of interest: delta, thetas, alpha , and beta. Our results indicated that hemispheric correlation distinctly changes with the gross sleep type (REM/NREM) as well as with different sleep stages (stages 1-4) within NREM. It also varies in the presence of arousal events and apnea. These results may provide a basis for novel insights into the functional asymmetries of brain in sleep and sleep associated events such as arousals and apnea.

Variations of snoring properties with macro sleep stages in a population of Obstructive Sleep Apnea patients

Snoring is common in Obstructive Sleep Apnea (OSA) patients. Snoring originates from the vibration of soft tissues in the upper airways (UA). Frequent UA collapse in OSA patients leads to sleep disturbances and arousal. In a routine sleep diagnostic procedure, sleep is broadly divided into rapid eye movement (REM), non-REM (NREM) states. These Macro-Sleep States (MSS) are known to be involved with different neuromuscular activities. These differences should influence the UA mechanics in OSA patients as well as the snoring sound (SS). In this paper, we propose a logistic regression model to investigate whether the properties of SS from OSA patients can be separated into REM/NREM group. Analyzing mathematical features of more than 500 SS events from 7 OSA patients, the model achieved 76% (± 0.10) sensitivity and 75% (± 0.10) specificity in categorizing REM and NREM related snores. These results indicate that snoring is affected by REM/NREM states and proposed method has potential in differentiating MSS.

Dynamic Data Analysis in Obstructive Sleep Apnea

Obstructive Sleep Apnea (OSA) is a serious disease caused by the collapse of upper airways during sleep. The present method of measuring the severity of OSA is the Apnea Hypopnea Index (AHI). The AHI is defined as the average number of Obstructive events (Apnea and Hypopnea, OAH-events) during the total sleep period. The number of occurrence of OAH events during each hour of sleep is a random variable with an unknown probability density function. Thus the measure AHI alone is insufficient to describe its true nature. We propose a new measure Dynamic Apnea Hypopnea Index Time Series (DAHI), which captures the temporal density of Apnea event over shorter time intervals, and use its higher moments to obtain a dynamic characterization of OSA

Left-right information flow in the brain during EEG arousals

In disorders such as sleep apnea, sleep is fragmented with frequent EEG-arousal (EEGA) as determined via changes in the sleep-electroencephalogram. EEGA is a poorly understood, complicated phenomenon which is critically important in studying the mysteries of sleep. In this paper we study the information flow between the left and right hemispheres of the brain during the EEGA as manifested through inter-hemispheric asynchrony (IHA) of the surface EEG. EEG data (using electrodes A1/C4 and A2/C3 of international 10-20 system) was collected from 5 subjects undergoing routine polysomnography (PSG). Spectral correlation coefficient (R) was computed between EEG data from two hemispheres for delta-delta(0.5-4 Hz), theta-thetas(4.1-8 Hz), alpha-alpha(8.1-12 Hz) & beta-beta(12.1-25 Hz) frequency bands, during EEGA events. EEGA were graded in 3 levels as (i) micro arousals (3-6 s), (ii) short arousals (6.1-10 s), & (iii) long arousals (10.1-15 s). Our results revealed that in beta band, IHA increases above the baseline after the onset of EEGA and returns to the baseline after the conclusion of event. Results indicated that the duration of EEGA events has a direct influence on the onset of IHA. The latency (L) between the onset of arousals and IHA were found to be L=2plusmn0.5 s (for micro arousals), 4plusmn2.2 s (short arousals) and 6.5plusmn3.6 s (long arousals)

Characterization of REM/NREM sleep using breath sounds in OSA

Obstructive Sleep Apnea (OSA) is a serious sleep disorder where patient experiences frequent upper airway collapse leading to breathing obstructions and arousals. Severity of OSA is assessed by averaging the number of incidences throughout the sleep. In a routine OSA diagnosis test, overnight sleep is broadly categorized into rapid eye movement (REM) and non-REM (NREM) stages and the number of events are considered accordingly to calculate the severity. A typical respiratory event is mostly accompanied by sounds such as loud breathing or snoring interrupted by choking, gasps for air. However, respiratory controls and ventilations are known to differ with sleep states. In this study, we assumed that the effect of sleep on respiration will alter characteristics of respiratory sounds as well as snoring in OSA patients. Our objective is to investigate whether the characteristics are sufficient to label snores of REM and NREM sleep. For investigation, we collected overnight audio recording from 12 patients undergoing routine OSA diagnostic test. We derived features from snoring sounds and its surrounding audio signal. We computed time series statistics such as mean, variance, inter-quartile-range to capture distinctive pattern from REM and NREM snores. We designed a Naive Bayes classifier to explore the usability of patterns to predict corresponding sleep states. Our method achieved a sensitivity of 92% (±9%) and specificity of 81% (±9%) in labeling snores into REM/NREM group which indicates the potential of snoring sounds to differentiate sleep states. This may be valuable to develop non-contact snore based technology for OSA diagnosis.

Cough Sound Analysis - A new tool for diagnosing Pneumonia

Pneumonia kills over 1,800,000 children annually throughout the world. Prompt diagnosis and proper treatment are essential to prevent these unnecessary deaths. Reliable diagnosis of childhood pneumonia in remote regions is fraught with difficulties arising from the lack of field-deployable imaging and laboratory facilities as well as the scarcity of trained community healthcare workers. In this paper, we present a pioneering class of enabling technology addressing both of these problems. Our approach is centered on automated analysis of cough and respiratory sounds, collected via microphones that do not require physical contact with subjects. We collected cough sounds from 91 patients suspected of acute respiratory illness such as pneumonia, bronchiolitis and asthma. We extracted mathematical features from cough sounds and used them to train a Logistic Regression classifier. We used the clinical diagnosis provided by the paediatric respiratory clinician as the gold standard to train and validate our classifier against. The methods proposed in this paper could separate pneumonia from other diseases at a sensitivity and specificity of 94% and 75% respectively, based on parameters extracted from cough sounds alone. Our method has the potential to revolutionize the management of childhood pneumonia in remote regions of the world.