Speed of Sound-Based Capnographic Sensor With Second-Generation CNN for Automated Classification of Cardiorespiratory Abnormalities

Bhagya, D; Manikandan, S.

doi:10.1109/jsen.2019.2921862

Cited by 7 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The influence of RNN proves that, with sufficient training, RNN can effectively predict the diagnosis of heart failure. The literature in [ 12 ] collected carbon dioxide respiratory concentration data of patients with chronic obstructive pulmonary disease, heart failure patients, and normal subjects and used convolutional neural networks to classify the carbon dioxide respiratory concentration data for chronic obstructive pulmonary disease/health, heart failure/health, and chronic obstructive pulmonary disease/heart failure classification. In [ 13 ], the authors have proposed a deep learning based algorithm for heart failure recognition, which is based on electrocardiogram, and used logistic regression and random forest model for comparison.…”

Section: Related Workmentioning

confidence: 99%

ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure

Chen

Huang

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Usually, heart failure occurs when heart-related diseases are developed and continue to deteriorate veins and arteries. Heart failure is the final stage of heart disease, and it has become an important medical problem, particularly among the aging population. In medical diagnosis and treatment, the examination of heart failure contains various indicators such as electrocardiogram. It is one of the relatively common ways to collect heart failure or attack related information and is also used as a reference indicator for doctors. Electrocardiogram indicates the potential activity of patient’s heart and directly reflects the changes in it. In this paper, a deep learning-based diagnosis system is presented for the early detection of heart failure particularly in elderly patients. For this purpose, we have used two datasets, Physio-Bank and MIMIC-III, which are publicly available, to extract ECG signals and thoroughly examine heart failure. Initially, a heart failure diagnosis model which is based on attention convolutional neural network (CBAM-CNN) is proposed to automatically extract features. Additionally, attention module adaptively learns the characteristics of local features and efficiently extracts the complex features of the ECG signal to perform classification diagnosis. To verify the exceptional performance of the proposed network model, various experiments were carried out in the realistic environment of hospitals. Influence of signal preprocessing on the performance of model is also discussed. These results show that the proposed CBAM-CNN model performance is better for both classifications of ECG signals. Likewise, the CBAM-CNN model is sensitive to noise, and its accuracy is effectively improved as soon as signal is refined.

show abstract

Section: Related Workmentioning

confidence: 99%

ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure

Chen

Huang

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…We showed in earlier work [13] that simple machine-learning algorithms (specifically, quadratic discriminant analysis) operating on just 4 Tcap waveform features from each of 80 exhalations in a record can distinguish between patients with congestive heart failure (CHF) and those with COPD, with accuracies around 75-80%. Some recent work using machine-learning methods, [14] and related papers by the same authors, reports significantly better results for this task, but differences in data selection/labeling make comparison with [13] difficult.…”

Section: B Prior Work On Modeling Tcap and Vcapmentioning

confidence: 99%

Low-Order Mechanistic Models for Volumetric and Temporal Capnography: Development, Validation, and Application

Murray

You

Verghese

et al. 2023

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

Develop low-order mechanistic models accounting quantitatively for, and identifiable from, the capnogram -the CO2 concentration in exhaled breath, recorded over time (Tcap) or exhaled volume (Vcap). Methods: The airflow model's single "alveolar" compartment has compliance and inertance, and feeds a resistive unperfused airway comprising a laminarflow region followed by a turbulent-mixing region. The gas-mixing model tracks mixing-region CO2 concentration, which is fitted breath-by-breath to the measured capnogram, yielding estimates of model parameters that characterize the capnogram. Results: For the 17 examined records (310 breaths) of airflow, airway pressure and Tcap from ventilated adult patients, the models fit closely (mean rmse 1% of end-tidal CO2 concentration on Vcap; 1.7% on Tcap). The associated parameters (4 for Vcap, 5 for Tcap) for each exhalation, and airflow parameters for the corresponding forced inhalation, are robustly estimated, and consonant with each other and literature values. The models also allow, using Tcap alone, estimation of the entire exhaled airflow waveform to within a scaling. This suggests new Tcap-based tests, analogous to spirometry but with normal breathing, for discriminating chronic obstructive pulmonary disease (COPD) from congestive heart failure (CHF). A version trained on 15 exhalations from each of 24 COPD/24 CHF Tcap records from one hospital, then tested 100 times with 15 random exhalations from each of 27 COPD/31 CHF Tcap records at another, gave mean accuracy 80.6% (stdev 2.1%). Another version, tested on 29 COPD/32 CHF, yielded AUROC 0.84. Conclusion: Our mechanistic models closely fit Tcap and Vcap measurements, and yield subject-specific parameter estimates. Significance: This can inform cardiorespiratory care.

show abstract

“…Wu et al [11] have developed a neural model based on 1-D CNN for analyzing human knee movement. In a recent work, the authors have developed a deep learning model by remodeling the 2-D CNN design to make it feasible for 1-D applications [12]. Their model was used for identifying cardiorespiratory abnormalities, and they attained a prediction accuracy of around 96%.…”

Section: Introductionmentioning

confidence: 99%

Time Series Classification-Based Correlational Neural Network With Bidirectional LSTM for Automated Detection of Kidney Disease

2021

Self Cite

View full text Add to dashboard Cite

In this paper, we aim to explore the feasibility of salivary analysis for Chronic Kidney Disease (CKD) detection and thereby design an automated mechanism to detect CKD through analysis of human saliva samples. We have implemented an improved deep learning model that combines both a one-dimensional Correlational Neural Network (1-D CorrNN) and bidirectional Long Short-Term Memory (LSTM) network for making accurate predictions. The LSTM network is integrated with the neural model to utilize the capabilities of both these networks to analyze the time-series data. The proposed model is trained and tested with a CKD sensing module. The application of deep learning algorithms helps to improve the detection accuracy as they are capable of discovering the best features from the input data. The proposed method achieved an average accuracy rate of 98.08% for the testing dataset. The results show that the proposed detection module and classification algorithm substantially advance the current methodologies, and provides more accurate predictions compared to conventional methods.

show abstract

Speed of Sound-Based Capnographic Sensor With Second-Generation CNN for Automated Classification of Cardiorespiratory Abnormalities

Cited by 7 publications

References 21 publications

ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure

ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure

Low-Order Mechanistic Models for Volumetric and Temporal Capnography: Development, Validation, and Application

Time Series Classification-Based Correlational Neural Network With Bidirectional LSTM for Automated Detection of Kidney Disease

Contact Info

Product

Resources

About