Exhaled breath monitoring during home ventilo-therapy in COPD patients by a new distributed tele-medicine system

Radogna, Antonio Vincenzo; Fiore, Nicola; Tumolo, Maria Rosaria; Luca, Valerio De; Paolis, Lucio Tommaso De; Guarino, Roberto; Leo, Carlo Giacomo; Mincarone, Pierpaolo; Sabato, Eugenio; Satriano, Francesco; Capone, S.; Sabina, Saverio

doi:10.1007/s12652-019-01618-2

Cited by 19 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We can also apply different data augmentation techniques to improve its performance. Our system can be combined with a Breath Monitoring System (Radogna et al, 2019) to make the process of detecting COPD even easier. Also, making this system attack resistant and more privacy preserved would be of utmost importance (Shaikh & Patil, 2018;Patil, Shashank & Deepali, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…The related work section describes automation methods and physical systems to ease the detection of respiratory diseases. Physical devices like the Breath Monitoring System (Radogna et al, 2019) propose a smart breath analysis device that can be used to detect COPD. A classifier based on combination of ANN and backpropagation based Multi-Layer Perceptron algorithm to predict patients respiratory audio crucial event with certain respiratory diseases, predominantly asthma and COPD, was examined (Khatri & Tamil, 2018).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease

Srivastava

Jain

Miranda

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain’s challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease

Srivastava

Jain

Miranda

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…We can also apply different data augmentation techniques to improve its performance. Our system can be combined with a Breath Monitoring System (Radogna et al 2019) to make the process of detecting COPD even easier. Also, making this system attack resistant and more privacy preserved would be of utmost importance.…”

Section: Discussionmentioning

confidence: 99%

“…The related work section describes automation methods and physical systems to ease the detection of respiratory diseases. Physical devices like the Breath Monitoring System (Radogna et al 2019) propose a smart breath analysis device that can be used to detect COPD. A classifier based on combination of ANN and backpropagation based Multi-Layer Perceptron algorithm to predict patients respiratory audio crucial event with certain respiratory diseases, predominantly asthma, and COPD, was examined (Khatri and Tamil 2018).…”

Section: Related Workmentioning

confidence: 99%

Peer Review #2 of "Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease (v0.1)"

2021

View full text Add to dashboard Cite

In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain's challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, gradually, respiratory diseases' impact is also on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply CNN based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary Disease (COPD) detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q), and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied Kfold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.

show abstract

“…We intend to provide COPD patients an efficient companion for multi-parameter monitoring and real-time data transmission. However, patients can use our prototype also within sensor platforms [73,74]. Nevertheless, with respect to the convenient wearability of the prototype, it can be applied for safety monitoring of workers in construction sites without interfering in the daily working routine and reducing the maneuverability.…”

Section: Discussionmentioning

confidence: 99%

Hardware Prototype for Wrist-Worn Simultaneous Monitoring of Environmental, Behavioral, and Physiological Parameters

et al. 2020

View full text Add to dashboard Cite

We designed a low-cost wrist-worn prototype for simultaneously measuring environmental, behavioral, and physiological domains of influencing factors in healthcare. Our prototype continuously monitors ambient elements (sound level, toxic gases, ultraviolet radiation, air pressure, temperature, and humidity), personal activity (motion tracking and body positioning using gyroscope, magnetometer, and accelerometer), and vital signs (skin temperature and heart rate). An innovative three-dimensional hardware, based on the multi-physical-layer approach is introduced. Using board-to-board connectors, several physical hardware layers are stacked on top of each other. All of these layers consist of integrated and/or add-on sensors to measure certain domain (environmental, behavioral, or physiological). The prototype includes centralized data processing, transmission, and visualization. Bi-directional communication is based on Bluetooth Low Energy (BLE) and can connect to smartphones as well as smart cars and smart homes for data analytic and adverse-event alerts. This study aims to develop a prototype for simultaneous monitoring of the all three areas for monitoring of workplaces and chronic obstructive pulmonary disease (COPD) patients with a concentration on technical development and validation rather than clinical investigation. We have implemented 6 prototypes which have been tested by 5 volunteers. We have asked the subjects to test the prototype in a daily routine in both indoor (workplaces and laboratories) and outdoor. We have not imposed any specific conditions for the tests. All presented data in this work are from the same prototype. Eleven sensors measure fifteen parameters from three domains. The prototype delivers the resolutions of 0.1 part per million (PPM) for air quality parameters, 1 dB, 1 index, and 1 °C for sound pressure level, UV, and skin temperature, respectively. The battery operates for 12.5 h under the maximum sampling rates of sensors without recharging. The final expense does not exceed 133€. We validated all layers and tested the entire device with a 75 min recording. The results show the appropriate functionalities of the prototype for further development and investigations.

show abstract

Exhaled breath monitoring during home ventilo-therapy in COPD patients by a new distributed tele-medicine system

Cited by 19 publications

References 27 publications

Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease

Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease

Peer Review #2 of "Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease (v0.1)"

Hardware Prototype for Wrist-Worn Simultaneous Monitoring of Environmental, Behavioral, and Physiological Parameters

Contact Info

Product

Resources

About