Electrocardiogram and Phonocardiogram Monitoring System for Cardiac Auscultation

Estimation of systolic blood pressure by Random Forest using heart sounds and a ballistocardiogram

Cuffless blood pressure measurement enables unobtrusive and continuous monitoring that can be integrated with wearable devices to extend healthcare to non-hospital settings. Most of the current research has focused on the estimation of blood pressure based on pulse transit time or pulse arrival time using ECG or peripheral cardiac pulse signals as proximal time references. This study proposed the use of a phonocardiogram (PCG) and ballistocardiogram (BCG), two signals detected noninvasively, to estimate systolic blood pressure (SBP). For this, the PCG and the BCG were simultaneously measured in 21 volunteers in the rest, activity, and post-activity conditions. Different features were considered based on the relationships between these signals. The intervals between S1 and S2 of the PCG and the I, J, and K waves of the BCG were statistically analyzed. The IJ and JK slopes were also estimated as additional features to train the machine-learning algorithm. The intervals S1-J, S1-K, S1-I, J-S2, and I-S2 were negatively correlated with changes in SBP (p-val < 0.01). The features were used as explanatory variables for a regressor based on the Random Forest. It was possible to estimate the systolic blood pressure with a mean error of 3.3 mmHg with a standard deviation of ± 5 mmHg. Therefore, we foresee that this proposal has potential applications for wearable devices that use low-cost embedded systems.

SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database

It has proved that the auscultation of respiratory sound has advantage in early respiratory diagnosis. Various methods have been raised to perform automatic respiratory sound analysis to reduce subjective diagnosis and physicians' workload. However, these methods highly rely on the quality of respiratory sound database. In this work, we have developed the first open-access paediatric respiratory sound database, SPRSound. The database consists of 2,683 records and 9,089 respiratory sound events from 292 participants. Accurate label is important to achieve a good prediction for adventitious respiratory sound classification problem. A custom-made sound label annotation software (SoundAnn) has been developed to perform sound editing, sound annotation, and quality assurance evaluation. A team of 11 experienced paediatric physicians is involved in the entire process to establish golden standard reference for the dataset. To verify the robustness and accuracy of the classification model, we have investigated the effects of different feature extraction methods and machine learning classifiers on the classification performance of our dataset. As such, we have achieved a score of 75.22%, 61.57%, 56.71%, and 37.84% for the four different classification challenges at the event level and record level.

Assessing Hemorrhagic Shock Severity Using the Second Heart Sound Determined from Phonocardiogram: A Novel Approach

Introduction: Hemorrhagic shock (HS) is a severe medical emergency. Early diagnosis of HS is important for clinical treatment. In this paper, we report a flexible material-based heart sound monitoring device which can evaluate the degree of HS through a phonocardiogram (PCG) change. Methods: Progressive hemorrhage treatments (H1, H2, and H3 stage) were used in swine to build animal models. The PCG sensor was mounted on the chest of the swine. Routine monitoring was used at the same time. Results: This study showed that arterial blood pressure decreased significantly from the H1 phase, while second heart sound amplitude (S2A) and energy (S2E) decreased significantly from the H2 phase. Both S2A and S2E correlated well with BP (p < 0.001). The heart rate, pulse pressure variation and serum hemoglobin level significantly changed in the H3 stage (p < 0.05). Discussion: The change of second heart sound (S2) was at the H2 stage and was earlier than routine monitoring methods. Therefore, PCG change may be a new indicator for the early detection of HS severity.

The Recognition Method of Athlete Exercise Intensity Based on ECG and PCG

Athletes usually arrange their training plans and determine their training intensity according to the coach's experience and simple physical indicators such as heart rate during exercise. However, the accuracy of this method is poor, and the training plan and exercise intensity arranged according to this method can easily cause physical damage, or the training cannot meet the actual needs. Therefore, in order to realize the reasonable arrangement and monitoring of athletes' training, a method of human exercise intensity recognition based on ECG (electrocardiogram) and PCG (Phonocardiogram) is proposed. First, the ECG and PCG signals are fused into a two-dimensional image, and the dataset is marked and divided according to the different motion intensities. Then, the training set is trained with a CNN (convolutional neural network) to obtain the prediction model of the neural network. Finally, the neural network model is used to identify the ECG and PCG signals to judge the exercise intensity of the athlete, so as to adjust the training plan according to the exercise intensity. The recognition accuracy of the model on the dataset can reach 95.68%. Compared with the use of heart rate to detect the physical state during exercise, ECG records the total potential changes in the process of depolarization and repolarization of the heart, and PCG records the waveform of the beating sound of the heart, which contains richer feature information. Combined with the CNN method, the athlete's exercise intensity prediction model constructed by extracting the features of the athlete's ECG and PCG signals realizes the real-time monitoring of the athlete's exercise intensity and has high accuracy and generalization ability.

High-Pass Sigma-Delta Modulator With Techniques of Operational Amplifier Sharing and Programmable Feedforward Coefficients for ECG Signal Acquisition

A high-pass sigma-delta modulator (HPSDM) is proposed for electrocardiography (ECG) signal acquisition system. The HPSDM is implemented using operational amplifier (op-amp) sharing and programmable feedforward coefficients. The op-amp sharing is adopted to reduce the quantity of amplifiers because they dominate the power consumption of the HPSDM. In addition, given that the magnitude of the ECG is dependent on different persons, programmable feedforward coefficients are utilized to extend the dynamic range of the HPSDM to fit the actual application. The proposed HPSDM is fabricated in a 0.18-μm standard CMOS process. Measurement results reveal that the proposed HPSDM has a signal-to-noise and distortion ratio (SNDR) of 54.5 dB and a power consumption of 2.25 μW under a 1.2 V supply voltage and achieves a figure of merit (FoM) of 12.96 pJ/conv. Moreover, the proposed HPSDM has an SNDR of 64.8 dB and a power consumption of 5.2 μW under a 1.8 V supply voltage and achieves a FoM of 9.15 pJ/conv due to the op-amp sharing technique. Under the 1.2 V and 1.8 V supply voltages, the dynamic range of the HPSDM is extended to approximately 12 dB due to the technique of programmable feedforward coefficients.

A Portable Wireless Urine Detection System With Power-Efficient Electrochemical Readout ASIC and ABTS-CNT Biosensor for UACR Detection

This work presents a portable wireless urine detection system which consists of an electrochemical readout application specific integrated circuit (ASIC) and a biosensor composed of 2, 2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) and carbon nanotube (ABTS-CNT) for the detection of urine albumin-to-creatinine ratio (UACR). The ASIC includes a potentiostat, a digital circuitry and a power management circuit which can perform electrochemistry techniques with a dual-channel screen-printing carbon electrode (SPCE). Electrochemical experiments on the proposed biosensor (SPCE|ABTS-CNT|Nafion) have revealed promising sensing characteristics for creatinine and human serum albumin detection. Practical urine tests has demonstrated the capability of the proposed urine detection system for UACR detection with both the power-efficient readout ASIC and the ABTS-CNT biosensor. A user-friendly prototype has also been designed which can be useful for either personal health administrationor homecare.

Temporal-Framing Adaptive Network for Heart Sound Segmentation Without Prior Knowledge of State Duration

OBJECTIVE

This paper presents a novel heart sound segmentation algorithm based on Temporal-Framing Adaptive Network (TFAN), including state transition loss and dynamic inference.

METHODS

In contrast to previous state-of-the-art approaches, TFAN does not require any prior knowledge of the state duration of heart sounds and is therefore likely to generalize to non sinus rhythm. TFAN was trained on 50 recordings randomly chosen from Training set A of the 2016 PhysioNet/Computer in Cardiology Challenge and tested on the other 12 independent databases (2,099 recordings and 52,180 beats). And further testing of performance was conducted on databases with three levels of increasing difficulty (LEVEL-I, -II and -III).

RESULTS

TFAN achieved a superior F₁ score for all 12 databases except for 'Test-B,' with an average of 96.72%, compared to 94.56% for logistic regression hidden semi-Markov model (LR-HSMM) and 94.18% for bidirectional gated recurrent neural network (BiGRNN). Moreover, TFAN achieved an overall F₁ score of 99.21%, 94.17%, 91.31% on LEVEL-I, -II and -III databases respectively, compared to 98.37%, 87.56%, 78.46% for LR-HSMM and 99.01%, 92.63%, 88.45% for BiGRNN.

CONCLUSION

TFAN therefore provides a substantial improvement on heart sound segmentation while using less parameters compared to BiGRNN.

SIGNIFICANCE

The proposed method is highly flexible and likely to apply to other non-stationary time series. Further work is required to understand to what extent this approach will provide improved diagnostic performance, although it is logical to assume superior segmentation will lead to improved diagnostics.