Contactless analysis of heart rate variability during cold pressor test using radar interferometry and bidirectional LSTM networks.

Contactless measurement of heart rate variability (HRV), which reflects changes of the autonomic nervous system (ANS) and provides crucial information on the health status of a person, would provide great benefits for both patients and doctors during prevention and aftercare. However, gold standard devices to record the HRV, such as the electrocardiograph, have the common disadvantage that they need permanent skin contact with the patient. Being connected to a monitoring device by cable reduces the mobility, comfort, and compliance by patients. Here, we present a contactless approach using a 24 GHz Six-Port-based radar system and an LSTM network for radar heart sound segmentation. The best scores are obtained using a two-layer bidirectional LSTM architecture. To verify the performance of the proposed system not only in a static measurement scenario but also during a dynamic change of HRV parameters, a stimulation of the ANS through a cold pressor test is integrated in the study design. A total of 638 minutes of data is gathered from 25 test subjects and is analysed extensively. High F-scores of over 95% are achieved for heartbeat detection. HRV indices such as HF norm are extracted with relative errors around 5%. Our proposed approach is capable to perform contactless and convenient HRV monitoring and is therefore suitable for long-term recordings in clinical environments and home-care scenarios.

Continuous In-Bed Monitoring of Vital Signs Using a Multi Radar Setup for Freely Moving Patients.

In hospitals, continuous monitoring of vital parameters can provide valuable information about the course of a patient's illness and allows early warning of emergencies. To enable such monitoring without restricting the patient's freedom of movement and comfort, a radar system is attached under the mattress which consists of four individual radar modules to cover the entire width of the bed. Using radar, heartbeat and respiration can be measured without contact and through clothing. By processing the raw radar data, the presence of a patient can be determined and movements are categorized into the classes "bed exit", "bed entry", and "on bed movement". Using this information, the vital parameters can be assessed in sections where the patient lies calmly in bed. In the first step, the presence and movement classification is demonstrated using recorded training and test data. Next, the radar was modified to perform vital sign measurements synchronized to a gold standard device. The evaluation of the individual radar modules shows that, regardless of the lying position of the test person, at least one of the radar modules delivers accurate results for continuous monitoring.

A dataset of clinically recorded radar vital signs with synchronised reference sensor signals.

Using Radar it is possible to measure vital signs through clothing or a mattress from the distance. This allows for a very comfortable way of continuous monitoring in hospitals or home environments. The dataset presented in this article consists of 24 h of synchronised data from a radar and a reference device. The implemented continuous wave radar system is based on the Six-Port technology and operates at 24 GHz in the ISM band. The reference device simultaneously measures electrocardiogram, impedance cardiogram and non-invasive continuous blood pressure. 30 healthy subjects were measured by physicians according to a predefined protocol. The radar was focused on the chest while the subjects were lying on a tilt table wired to the reference monitoring device. In this manner five scenarios were conducted, the majority of them aimed to trigger hemodynamics and the autonomic nervous system of the subjects. Using the database, algorithms for respiratory or cardiovascular analysis can be developed and a better understanding of the characteristics of the radar-recorded vital signs can be gained.

A dataset of radar-recorded heart sounds and vital signs including synchronised reference sensor signals.

Radar systems allow for contactless measurements of vital signs such as heart sounds, the pulse signal, and respiration. This approach is able to tackle crucial disadvantages of state-of-the-art monitoring devices such as the need for permanent wiring and skin contact. Potential applications include the employment in a hospital environment but also in home care or passenger vehicles. This dataset consists of synchronised data which are acquired using a Six-Port-based radar system operating at 24 GHz, a digital stethoscope, an ECG, and a respiration sensor. 11 test subjects were measured in different defined scenarios and at several measurement positions such as at the carotid, the back, and several frontal positions on the thorax. Overall, around 223 minutes of data were acquired at scenarios such as breath-holding, post-exercise measurements, and while speaking. The presented dataset contains reference-labeled ECG signals and can therefore easily be used to either test algorithms for monitoring the heart rate, but also to gain insights about characteristic effects of radar-based vital sign monitoring.

Automatic Signal Quality Index Determination of Radar-Recorded Heart Sound Signals Using Ensemble Classification.

OBJECTIVE: Radar technology promises to be a touchless and thereby burden-free method for continuous heart sound monitoring, which can be used to detect cardiovascular diseases. However, the first and most crucial step is to differentiate between high- and low-quality segments in a recording to assess their suitability for a subsequent automated analysis. This paper gives a comprehensive study on this task and first addresses the specific characteristics of radar-recorded heart sound signals. METHODS: To gather heart sound signals recorded from radar, a bistatic radar system was built and installed at the university hospital. Under medical supervision, heart sound data were recorded from 30 healthy test subjects. The signals were segmented and labeled as high- or low-quality by a medical expert. Different state-of-the-art pattern classification algorithms were evaluated for the task of automated signal quality determination and the most promising one was optimized and evaluated using leave-one-subject-out cross validation. RESULTS: The proposed classifier is able to achieve an accuracy of up to 96.36% and demonstrates a superior classification performance compared with the state-of-the-art classifier with a maximum accuracy of 76.00%. CONCLUSION: This paper introduces an ensemble classifier that is able to perform automated signal quality determination of radar-recorded heart sound signals with a high accuracy. SIGNIFICANCE: Besides achieving a higher performance compared with state-of-the-art classifiers, this study is the first one to deal with the quality determination of heart sounds that are recorded by radar systems. The proposed method enables contactless and continuous heart sound monitoring for the detection of cardiovascular diseases.

A Clinically Evaluated Interferometric Continuous-Wave Radar System for the Contactless Measurement of Human Vital Parameters.

Vital parameters are key indicators for the assessment of health. Conventional methods rely on direct contact with the patients' skin and can hence cause discomfort and reduce autonomy. This article presents a bistatic 24 GHz radar system based on an interferometric six-port architecture and features a precision of 1 µm in distance measurements. Placed at a distance of 40 cm in front of the human chest, it detects vibrations containing respiratory movements, pulse waves and heart sounds. For the extraction of the respiration rate, time-domain approaches like autocorrelation, peaksearch and zero crossing rate are compared to the Fourier transform, while template matching and a hidden semi-Markov model are utilized for the detection of the heart rate from sphygmograms and heart sounds. A medical study with 30 healthy volunteers was conducted to collect 5.5 h of data, where impedance cardiogram and electrocardiogram were used as gold standard for synchronously recording respiration and heart rate, respectively. A low root mean square error for the breathing rate (0.828 BrPM) and a high overall F1 score for heartbeat detection (93.14%) could be achieved using the proposed radar system and signal processing.

Respiration Extraction from Radar Heart Sound Measurements.

Radar-based respiration measurement is susceptible to upper body movement in addition to the respiratory motion of the chest. This parasitic movement can only be canceled using a dual radar system from the front and back. However, the larger hardware effort could be avoided if a physiological parameter is measured that is influenced by respiration only but not by the movement. This could then be used to indirectly derive breathing. In this paper a method is presented, how the respiration can be deduced despite of upper body movements. To achieve this, a Six-Port interferometer is used to measure the heart sound envelogram of a test person from which subsequently the respiration can be reconstructed.

Segmentation of Radar-Recorded Heart Sound Signals Using Bidirectional LSTM Networks.

Sounds caused by the action of the heart reflect both its health as well as deficiencies and are examined by physicians since antiquity. Pathologies of the valves, e.g. insufficiencies and stenosis, cardiac effusion, arrhythmia, inflammation of the surrounding tissue and other diagnosis can be reached by experienced physicians. However, practice is needed to assess the findings correctly. Furthermore, stethoscopes do not allow for long-term monitoring of a patient. Recently, radar technology has shown the ability to perform continuous touchless and thereby burden-free heart sound measurements. In order to perform automated classification of the signals, the first and most important step is to segment the heart sounds into their physiological phases. This paper examines the use of different Long Short-Term Memory (LSTM) architectures for this purpose based on a large dataset of radar-recorded heart sounds gathered from 30 different test persons in a clinical study. The best-performing network, a bidirectional LSTM, achieves a sample-wise accuracy of 93.4 % and a F1 score for the first heart sound of 95.8 %.

Radar-Based Heart Sound Detection.

This paper introduces heart sound detection by radar systems, which enables touch-free and continuous monitoring of heart sounds. The proposed measurement principle entails two enhancements in modern vital sign monitoring. First, common touch-based auscultation with a phonocardiograph can be simplified by using biomedical radar systems. Second, detecting heart sounds offers a further feasibility in radar-based heartbeat monitoring. To analyse the performance of the proposed measurement principle, 9930 seconds of eleven persons-under-tests' vital signs were acquired and stored in a database using multiple, synchronised sensors: a continuous wave radar system, a phonocardiograph (PCG), an electrocardiograph (ECG), and a temperature-based respiration sensor. A hidden semi-Markov model is utilised to detect the heart sounds in the phonocardiograph and radar data and additionally, an advanced template matching (ATM) algorithm is used for state-of-the-art radar-based heartbeat detection. The feasibility of the proposed measurement principle is shown by a morphology analysis between the data acquired by radar and PCG for the dominant heart sounds S1 and S2: The correlation is 82.97 ± 11.15% for 5274 used occurrences of S1 and 80.72 ± 12.16% for 5277 used occurrences of S2. The performance of the proposed detection method is evaluated by comparing the F-scores for radar and PCG-based heart sound detection with ECG as reference: Achieving an F1 value of 92.22 ± 2.07%, the radar system approximates the score of 94.15 ± 1.61% for the PCG. The accuracy regarding the detection timing of heartbeat occurrences is analysed by means of the root-mean-square error: In comparison to the ATM algorithm (144.9 ms) and the PCG-based variant (59.4 ms), the proposed method has the lowest error value (44.2 ms). Based on these results, utilising the detected heart sounds considerably improves radar-based heartbeat monitoring, while the achieved performance is also competitive to phonocardiography.