Automatic Audio-Based Classification of Patient Inhaler Use: A Pharmacy Based Study.

Chronic respiratory diseases may be controlled through the delivery of medication to the airways and lungs using an inhaler. However, adherence to correct inhaler technique is poor, which impedes patients from receiving maximum clinical benefit from their medication. In this study, the Inhaler Compliance Assessment device was employed to record audio of patients using a Diskus dry powder inhaler. An algorithm that classifies inhaler sounds (blister, inhalation, interference) was developed to automatically assess patient adherence from these inhaler audio recordings. The presented algorithm employed audio-based signal processing methods and statistical modeling in the form of quadratic discriminant analysis (QDA). A total of 350 audio recordings were obtained from 70 patients. The acquired audio dataset was split evenly for training and testing. A total accuracy of 85.35% was obtained (testing dataset) for this 3-class classification system. A sensitivity of 89.22% and 70% was obtained for inhalation and blister detection respectively. This approach may have significant clinical impact by providing healthcare professionals with an efficient, objective method of monitoring patient adherence to inhaler treatment.

A new computer-aided detection approach based on analysis of local and global mammographic feature asymmetry.

PURPOSE: This study aims to develop and test a new computer-aided detection (CAD) approach and scheme, assessing the likelihood of a subject harboring breast abnormalities. METHODS: The proposed scheme is based on the analysis of both local and global bilateral mammographic feature asymmetries. The level of local or global asymmetry is assessed by analyzing mammographic features extracted from the bilaterally matched regions of interest (ROIs), or from the entire breast, respectively. The selected local and global feature vectors are combined and classified using a maximum likelihood obtained from a naïve Bayes classifier. This scheme was evaluated using a leave-one-case-out cross-validation method that was applied to 243 subjects from mini-MIAS and INbreast databases. In addition, the result is compared with a conventional unilateral (or single) image-based CAD scheme. RESULTS: Using a case-based evaluation approach and an area under curve (AUC) of the receiver operating characteristic (ROC) as a performance index, the new scheme yielded AUC = 0.79 ± 0.07, an 8.2% increase compared with AUC = 0.73 ± 0.08 obtained using the unilateral image-based CAD scheme. CONCLUSIONS: This work demonstrates that applying bilateral asymmetry analysis increases the discriminatory power of CAD schemes while optimizing the likelihood assessment of breast abnormalities presence. Therefore, the proposed CAD approach provides the radiologist with beneficial supplementary information and can indicate high-risk cases.

Objective Assessment of Patient Inhaler User Technique Using an Audio-Based Classification Approach.

Many patients make critical user technique errors when using pressurised metered dose inhalers (pMDIs) which reduce the clinical efficacy of respiratory medication. Such critical errors include poor actuation coordination (poor timing of medication release during inhalation) and inhaling too fast (peak inspiratory flow rate over 90 L/min). Here, we present a novel audio-based method that objectively assesses patient pMDI user technique. The Inhaler Compliance Assessment device was employed to record inhaler audio signals from 62 respiratory patients as they used a pMDI with an In-Check Flo-Tone device attached to the inhaler mouthpiece. Using a quadratic discriminant analysis approach, the audio-based method generated a total frame-by-frame accuracy of 88.2% in classifying sound events (actuation, inhalation and exhalation). The audio-based method estimated the peak inspiratory flow rate and volume of inhalations with an accuracy of 88.2% and 83.94% respectively. It was detected that 89% of patients made at least one critical user technique error even after tuition from an expert clinical reviewer. This method provides a more clinically accurate assessment of patient inhaler user technique than standard checklist methods.

Advances in Audio-Based Systems to Monitor Patient Adherence and Inhaler Drug Delivery.

Hundreds of millions of people worldwide have asthma and COPD. Current medications to control these chronic respiratory diseases can be administered using inhaler devices, such as the pressurized metered dose inhaler and the dry powder inhaler. Provided that they are used as prescribed, inhalers can improve patient clinical outcomes and quality of life. Poor patient inhaler adherence (both time of use and user technique) is, however, a major clinical concern and is associated with poor disease control, increased hospital admissions, and increased mortality rates, particularly in low- and middle-income countries. There are currently limited methods available to health-care professionals to objectively and remotely monitor patient inhaler adherence. This review describes recent sensor-based technologies that use audio-based approaches that show promising opportunities for monitoring inhaler adherence in clinical practice. This review discusses how one form of sensor-based technology, audio-based monitoring systems, can provide clinically pertinent information regarding patient inhaler use over the course of treatment. Audio-based monitoring can provide health-care professionals with quantitative measurements of the drug delivery of inhalers, signifying a clear clinical advantage over other methods of assessment. Furthermore, objective audio-based adherence measures can improve the predictability of patient outcomes to treatment compared with current standard methods of adherence assessment used in clinical practice. Objective feedback on patient inhaler adherence can be used to personalize treatment to the patient, which may enhance precision medicine in the treatment of chronic respiratory diseases.

Spatial acoustic radiation of respiratory sounds for sleep evaluation.

Body posture has an effect on sleeping quality and breathing disorders and therefore it is important to be recognized for the completion of the sleep evaluation process. Since humans have a directional acoustic radiation pattern, it is hypothesized that microphone arrays can be used to recognize different body postures, which is highly practical for sleep evaluation applications that already measure respiratory sounds using distant microphones. Furthermore, body posture may have an effect on distant microphone measurement; hence, the measurement can be compensated if the body posture is correctly recognized. A spherical harmonics decomposition approach to the spatial acoustic radiation is presented, assuming an array of eight microphones in a medium-sized audiology booth. The spatial sampling and reconstruction of the radiation pattern is discussed, and a final setup for the microphone array is recommended. A case study is shown using recorded segments of snoring and breathing sounds of three human subjects in three body postures in a silent but not anechoic audiology booth.

Diurnal and seasonal variation of cough episodes in healthy young adults.

SETTING: We developed an algorithm to assess recorded cough episodes and differentiate them from similar, non-cough sounds. OBJECTIVE: To measure cough episodes in healthy young adults, cigarette smokers and non-smokers over a 24-hour recording period, during the course of normal activity. DESIGN: The study subjects were students, aged 20-40 years old. 24-hour sound recordings were conducted by a portable recorder. Analysis used an algorithm that was developed and tested in the study. RESULTS: Seventy students were recruited. Recordings included 2628 cough episodes in 1704 h of recording. The cough detection algorithm successfully detected 88.5% of recorded cough episodes and 95.6% of non-cough sounds. There was a clear tendency for more coughs among smokers. Autumn was the season with the highest mean cough episodes per day (58.65), while summer had the lowest (14.19). There was a strong correlation between self-reported cough episodes and recorded coughs. Cough episodes were significantly more frequent between noon and midnight (p < 0.0001). CONCLUSION: There is a very large range in daily coughs among healthy young adults. During sleeping hours there are less cough episodes. In autumn and spring there are more cough episodes compared to summer and winter, probably secondary to environmental factors. In smokers, the coughing rate is relatively high. If the cough detection device will be able to discriminate between cough variants (i.e., healthy versus patient), and stringent validation will confirm sensitivity and specificity, valuable data from this device may ease the decision regarding medications, or any other changes in order to improve outcome.

A new computer-aided detection scheme based on assessment of local bilateral mammographic feature asymmetry - a preliminary evaluation.

Accurate segmentation of breast lesions depicting on two-dimensional projection mammograms has been proven very difficult and unreliable. In this study we investigated a new approach of a computer-aided detection (CAD) scheme of mammograms without lesion segmentation. Our scheme was developed based on the detection and analysis of region-of-interest (ROI)-based bilateral mammographic tissue or feature asymmetry. A bilateral image registration, image feature selection process, and naïve Bayes linear classifier were implemented in CAD scheme. CAD performance predicting the likelihood of either an ROI or a subject (case) being abnormal was evaluated using 161 subjects from the mini-MIAS database and a leave-one-out testing method. The results showed that areas under receiver operating characteristic (ROC) curves were 0.87 and 0.72 on the ROI-based and case-based evaluation, respectively. The study demonstrated that using ROI-based bilateral mammographic tissue asymmetry can provide supplementary information with high discriminatory power in order to improve CAD performance.

Fall detection of elderly through floor vibrations and sound.

Falls are very prevalent among the elderly especially in their home. The statistics show that approximately one in every three adults 65 years old or older falls each year. Almost 30% of those falls result in serious injuries. Studies have shown that the medical outcome of a fall is largely dependent upon the response and rescue time. Therefore, reliable and immediate fall detection system is important so that adequate medical support could be delivered. We have developed a unique and inexpensive solution that does not require subjects to wear anything. The solution is based on floor vibration and acoustic sensing, and uses a pattern recognition algorithm to discriminate between human or inanimate object fall events. Using the proposed system we can detect human falls with a sensitivity of 95% and specificity of 95%.