1
|
Zhou W, Yu L, Zhang M, Xiao W. A low power respiratory sound diagnosis processing unit based on LSTM for wearable health monitoring. BIOMED ENG-BIOMED TE 2023; 68:469-480. [PMID: 37080905 DOI: 10.1515/bmt-2022-0421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 04/05/2023] [Indexed: 04/22/2023]
Abstract
Early prevention and detection of respiratory disease have attracted extensive attention due to the significant increase in people with respiratory issues. Restraining the spread and relieving the symptom of this disease is essential. However, the traditional auscultation technique demands a high-level medical skill, and computational respiratory sound analysis approaches have limits in constrained locations. A wearable auscultation device is required to real-time monitor respiratory system health and provides consumers with ease. In this work, we developed a Respiratory Sound Diagnosis Processor Unit (RSDPU) based on Long Short-Term Memory (LSTM). The experiments and analyses were conducted on feature extraction and abnormality diagnosis algorithm of respiratory sound, and Dynamic Normalization Mapping (DNM) was proposed to better utilize quantization bits and lessen overfitting. Furthermore, we developed the hardware implementation of RSDPU including a corrector to filter diagnosis noise. We presented the FPGA prototyping verification and layout of the RSDPU for power and area evaluation. Experimental results demonstrated that RSDPU achieved an abnormality diagnosis accuracy of 81.4 %, an area of 1.57 × 1.76 mm under the SMIC 130 nm process, and power consumption of 381.8 μW, which met the requirements of high accuracy, low power consumption, and small area.
Collapse
Affiliation(s)
- Weixin Zhou
- Chinese Academy of Sciences, Institute of Semiconductors, Beijing, China
| | - Lina Yu
- Chinese Academy of Sciences, Institute of Semiconductors, Beijing, China
| | - Ming Zhang
- Chinese Academy of Sciences, Institute of Semiconductors, Beijing, China
| | - Wan'ang Xiao
- Chinese Academy of Sciences, Institute of Semiconductors, Beijing, China
| |
Collapse
|
2
|
Redij R, Kaur A, Muddaloor P, Sethi AK, Aedma K, Rajagopal A, Gopalakrishnan K, Yadav A, Damani DN, Chedid VG, Wang XJ, Aakre CA, Ryu AJ, Arunachalam SP. Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems. SENSORS (BASEL, SWITZERLAND) 2023; 23:2302. [PMID: 36850899 PMCID: PMC9967043 DOI: 10.3390/s23042302] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
Production of bowel sounds, established in the 1900s, has limited application in existing patient-care regimes and diagnostic modalities. We review the physiology of bowel sound production, the developments in recording technologies and the clinical application in various scenarios, to understand the potential of a bowel sound recording and analysis device-the phonoenterogram in future gastroenterological practice. Bowel sound production depends on but is not entirely limited to the type of food consumed, amount of air ingested and the type of intestinal contractions. Recording technologies for extraction and analysis of these include the wavelet-based filtering, autoregressive moving average model, multivariate empirical mode decompression, radial basis function network, two-dimensional positional mapping, neural network model and acoustic biosensor technique. Prior studies evaluate the application of bowel sounds in conditions such as intestinal obstruction, acute appendicitis, large bowel disorders such as inflammatory bowel disease and bowel polyps, ascites, post-operative ileus, sepsis, irritable bowel syndrome, diabetes mellitus, neurodegenerative disorders such as Parkinson's disease and neonatal conditions such as hypertrophic pyloric stenosis. Recording and analysis of bowel sounds using artificial intelligence is crucial for creating an accessible, inexpensive and safe device with a broad range of clinical applications. Microwave-based digital phonoenterography has huge potential for impacting GI practice and patient care.
Collapse
Affiliation(s)
- Renisha Redij
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Avneet Kaur
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pratyusha Muddaloor
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Arshia K. Sethi
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Keirthana Aedma
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Keerthy Gopalakrishnan
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Ashima Yadav
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Devanshi N. Damani
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Internal Medicine, Texas Tech University Health Science Center, El Paso, TX 79995, USA
| | - Victor G. Chedid
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | - Xiao Jing Wang
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | - Shivaram P. Arunachalam
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
3
|
Mallegni N, Molinari G, Ricci C, Lazzeri A, La Rosa D, Crivello A, Milazzo M. Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare. BIOSENSORS 2022; 12:835. [PMID: 36290973 PMCID: PMC9599683 DOI: 10.3390/bios12100835] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/27/2022] [Accepted: 10/05/2022] [Indexed: 06/16/2023]
Abstract
Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.
Collapse
Affiliation(s)
- Norma Mallegni
- Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
| | - Giovanna Molinari
- Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
| | - Claudio Ricci
- Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
| | - Andrea Lazzeri
- Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
| | - Davide La Rosa
- ISTI-CNR, Institute of Information Science and Technologies, 56124 Pisa, Italy
| | - Antonino Crivello
- ISTI-CNR, Institute of Information Science and Technologies, 56124 Pisa, Italy
| | - Mario Milazzo
- Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy
| |
Collapse
|
4
|
Nowak JK, Nowak R, Radzikowski K, Grulkowski I, Walkowiak J. Automated Bowel Sound Analysis: An Overview. SENSORS (BASEL, SWITZERLAND) 2021; 21:5294. [PMID: 34450735 PMCID: PMC8400220 DOI: 10.3390/s21165294] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 07/31/2021] [Accepted: 08/03/2021] [Indexed: 11/24/2022]
Abstract
Despite technological progress, we lack a consensus on the method of conducting automated bowel sound (BS) analysis and, consequently, BS tools have not become available to doctors. We aimed to briefly review the literature on BS recording and analysis, with an emphasis on the broad range of analytical approaches. Scientific journals and conference materials were researched with a specific set of terms (Scopus, MEDLINE, IEEE) to find reports on BS. The research articles identified were analyzed in the context of main research directions at a number of centers globally. Automated BS analysis methods were already well developed by the early 2000s. Accuracy of 90% and higher had been achieved with various analytical approaches, including wavelet transformations, multi-layer perceptrons, independent component analysis and autoregressive-moving-average models. Clinical research on BS has exposed their important potential in the non-invasive diagnosis of irritable bowel syndrome, in surgery, and for the investigation of gastrointestinal motility. The most recent advances are linked to the application of artificial intelligence and the development of dedicated BS devices. BS research is technologically mature, but lacks uniform methodology, an international forum for discussion and an open platform for data exchange. A common ground is needed as a starting point. The next key development will be the release of freely available benchmark datasets with labels confirmed by human experts.
Collapse
Affiliation(s)
- Jan Krzysztof Nowak
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, 60-572 Poznan, Poland;
| | - Robert Nowak
- Artificial Intelligence Division, Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland; (R.N.); (K.R.)
| | - Kacper Radzikowski
- Artificial Intelligence Division, Institute of Computer Science, Warsaw University of Technology, 00-665 Warsaw, Poland; (R.N.); (K.R.)
| | - Ireneusz Grulkowski
- Faculty of Physics, Astronomy and Informatics, Institute of Physics, Nicolaus Copernicus University, 87-100 Toruń, Poland;
| | - Jaroslaw Walkowiak
- Department of Pediatric Gastroenterology and Metabolic Diseases, Poznan University of Medical Sciences, 60-572 Poznan, Poland;
| |
Collapse
|
5
|
Wang G, Wang M, Liu H, Zhao S, Liu L, Wang W. Changes in bowel sounds of inpatients undergoing general anesthesia. Biomed Eng Online 2020; 19:60. [PMID: 32731903 PMCID: PMC7392822 DOI: 10.1186/s12938-020-00805-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 07/24/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND General anesthesia can affect intestinal function, but there is no objective, practical and effective indicator to evaluate the inhibition and recovery of intestinal function. The main objectives of this study were to assess whether bowel sounds (BSs) changed before, immediately after and 3 h after general anesthesia, and whether these changes in BSs are an effective indicator of intestinal function and an accurate guide for postoperative feeding. METHODS We randomly selected 26 inpatients and collected three sets of 5-min continuous BS data before the operation (Pre-op), immediately after the operation (Pro-op) and 3 h after the operation (3 h-Pro-op) for each patient. Then, the linear and nonlinear characteristic values (CVs) of each effective bowel sound were extracted and paired t tests and rank-sum tests were used to evaluate the changes in the BSs. RESULTS The differences in CVs, between Pre-op and Pro-op, as well as between Pro-op and 3 h-Pro-op, were statistically significant (p < 0.05). However, there are no statistically significant differences between all the CVs between Pre-op and 3 h-Pro-op (p > 0.05). CONCLUSION BSs change before and after general anesthesia. Furthermore, the BSs are weakened due to general anesthesia and recover to the pre-op state 3 h later. Therefore, the BSs can be an indicator of intestinal function under general anesthesia, so as to provide guidance for postoperative feeding, which is of considerable clinical significance.
Collapse
Affiliation(s)
- Guojing Wang
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, China
- Department of Medical Engineering, Medical Care Center, Chinese PLA General Hospital, Beijing, China
- Bioengineering Research Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
| | - Mingjun Wang
- Anesthesia and Operation Center, Chinese PLA General Hospital, Beijing, China
| | - Hongyun Liu
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, China
- Department of Medical Engineering, Medical Care Center, Chinese PLA General Hospital, Beijing, China
- Bioengineering Research Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
| | - Suping Zhao
- College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
| | - Lu Liu
- College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Beijing, China
| | - Weidong Wang
- Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing, China.
- Department of Medical Engineering, Medical Care Center, Chinese PLA General Hospital, Beijing, China.
- Bioengineering Research Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China.
| |
Collapse
|
6
|
Kölle K, Fougner AL, Ellingsen R, Carlsen SM, Stavdahl Ø. Feasibility of Early Meal Detection Based on Abdominal Sound. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2019; 7:3300212. [PMID: 32309058 PMCID: PMC6824555 DOI: 10.1109/jtehm.2019.2940218] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 08/14/2019] [Accepted: 08/25/2019] [Indexed: 12/30/2022]
Abstract
In classical approaches for an artificial pancreas, continuous glucose monitoring (CGM) is the only measured variable used for insulin dosing and additional control functions. The CGM values are subject to time delays and slow dynamics between blood and the sensing location. These time lags compromise the controller's performance in maintaining (near to) normal glucose levels. Meal information could enhance the control outcome. However, meal announcement by the user is not reliable, and it takes 30 min to 40 min from meal onset until a meal is detected by methods based on CGM. In this pilot study, the use of bowel sounds for meal detection was investigated. In particular, we focused on whether bowel sounds change qualitatively during or shortly after meal ingestion. After fasting for at least 4 h, 11 healthy volunteers ingested a lunch meal at their usual time. Abdominal sound was recorded by a condenser microphone that was attached to the right upper quadrant of the abdomen by medical tape. Features that describe the power distribution over the frequency spectrum were extracted and used for classification by support vector machines. These classifiers were trained in a leave-one-out cross-validation scheme. Meals could be detected on average 10 min (std: 4.4 min) after they had started. Half of these were detected without false alarms. This shows that abdominal sound monitoring could provide an early meal detection. Further studies should investigate this possibility on a larger population in more general settings.
Collapse
Affiliation(s)
- Konstanze Kölle
- Department of Engineering CyberneticsNorwegian University of Science and Technology (NTNU)7491TrondheimNorway
- Department of EndocrinologySt. Olavs University Hospital7491TrondheimNorway
| | - Anders Lyngvi Fougner
- Department of Engineering CyberneticsNorwegian University of Science and Technology (NTNU)7491TrondheimNorway
| | - Reinold Ellingsen
- Department of Electronic SystemsNorwegian University of Science and Technology (NTNU)7491TrondheimNorway
| | - Sven Magnus Carlsen
- Department of EndocrinologySt. Olavs University Hospital7491TrondheimNorway
- Department of Clinical and Molecular MedicineNorwegian University of Science and Technology (NTNU)7491TrondheimNorway
| | - Øyvind Stavdahl
- Department of Engineering CyberneticsNorwegian University of Science and Technology (NTNU)7491TrondheimNorway
| |
Collapse
|
7
|
Kölle K, Aftab MF, Andersson LE, Fougner AL, Stavdahl Ø. Data driven filtering of bowel sounds using multivariate empirical mode decomposition. Biomed Eng Online 2019; 18:28. [PMID: 30894187 PMCID: PMC6425713 DOI: 10.1186/s12938-019-0646-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Accepted: 03/12/2019] [Indexed: 11/24/2022] Open
Abstract
Background The analysis of abdominal sounds can help to diagnose gastro-intestinal diseases. Sounds originating from the stomach and the intestine, the so-called bowel sounds, occur in various forms. They are described as loose successions or clusters of rather sudden bursts. Realistic recordings of abdominal sounds are contaminated with noise and artifacts from which the bowel sounds must be differentiated. Methods The proposed intrinsic mode function-fractal dimension (IMF-FD) filtering utilizes the property of the multivariate empirical mode decomposition (MEMD) to behave as a series of band pass filters. The MEMD decomposes the abdominal signal into its different frequency components. The resulting intrinsic mode functions (IMFs) are modulated in amplitude and frequency where transient sonic events occur. Based on the complexity of the IMFs, measured by their fractal dimension (FD) in sliding windows, the information-carrying IMFs are selected. The filtered signal is formed as the superposition of all selected IMFs. The IMF-FD filter not only enhances the non-linear components of the original signal but also segments them from the rest. Another important aspect of this work is that typical artifacts that occur in the same frequency range as bowel sounds can be subsequently eliminated by heuristic rules. Conclusions The method is tested on a realistic, contaminated data set with promising performance: close to 100% of the manually labeled bowel sounds are identified.
Collapse
Affiliation(s)
- Konstanze Kölle
- Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. .,Department of Endocrinology, St. Olavs University Hospital, Trondheim, Norway.
| | - Muhammad Faisal Aftab
- Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Leif Erik Andersson
- Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Anders Lyngvi Fougner
- Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Øyvind Stavdahl
- Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| |
Collapse
|