This paper proposes accurate respiratory rate estimation using nasal breath sound recordings from a smartphone. Specifically, the proposed method detects nasal airflow using a built-in smartphone ...microphone or a headset microphone placed underneath the nose. In addition, we also examined if tracheal breath sounds recorded by the built-in microphone of a smartphone placed on the paralaryngeal space can also be used to estimate different respiratory rates ranging from as low as 6 breaths/min to as high as 90 breaths/min. The true breathing rates were measured using inductance plethysmography bands placed around the chest and the abdomen of the subject. Inspiration and expiration were detected by averaging the power of nasal breath sounds. We investigated the suitability of using the smartphone-acquired breath sounds for respiratory rate estimation using two different spectral analyses of the sound envelope signals: The Welch periodogram and the autoregressive spectrum. To evaluate the performance of the proposed methods, data were collected from ten healthy subjects. For the breathing range studied (6-90 breaths/min), experimental results showed that our approach achieves an excellent performance accuracy for the nasal sound as the median errors were less than 1% for all breathing ranges. The tracheal sound, however, resulted in poor estimates of the respiratory rates using either spectral method. For both nasal and tracheal sounds, significant estimation outliers resulted for high breathing rates when subjects had nasal congestion, which often resulted in the doubling of the respiratory rates. Finally, we show that respiratory rates from the nasal sound can be accurately estimated even if a smartphone's microphone is as far as 30 cm away from the nose.
This paper presents a comprehensive study on the application of machine learning techniques in the prediction of respiratory rate via time-series-based statistical and machine learning methods using ...several physiological signals. Two different models, ARIMA and LSTM, were developed. The LSTM model showed a stronger capacity for learning and capturing complicated patterns in the data compared to the ARIMA model. The findings imply that LSTM models, by incorporating many variables, have the ability to provide predictions that are more accurate, particularly in situations where respiratory rate values vary significantly.
The photoplethysmographic (PPG) signal measures the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration, and other physiological ...effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), and respiratory rate (RR) and this will reduce the number of sensors connected to the patient's body for recording these vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR and RR simultaneously from PPG signal. To examine the performance of the proposed algorithm, we used 310 (from 35 subjects) and 632 (from 42 subjects) epochs of simultaneously recorded electrocardiogram, PPG, and respiratory signal extracted from MIMIC (Physionet ATM data bank) and Capnobase database, respectively. Results of EEMD-PCA-based extraction of HR and RR from PPG signal showed that the median RMS error (1st and 3rd quartiles) obtained in MIMIC data set for RR was 0.89 (0, 1.78) breaths/min, for HR was 0.57 (0.30, 0.71) beats/min and in Capnobase data set it was 2.77 (0.50, 5.9) breaths/min and 0.69 (0.54, 1.10) beats/min for RR and HR, respectively. These results illustrated that the proposed EEMD-PCA approach is more accurate in estimating HR and RR than other existing methods. Efficient and reliable extraction of HR and RR from the pulse oximeter's PPG signal will help patients for monitoring HR and RR with low cost and less discomfort.
Goal: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high- and low-quality input data, and fail to perform ...well on independent "validation" datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG. Methods: The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existing methods in the literature. Results: The proposed method achieved comparable accuracy to existing methods in the literature, with mean absolute errors (median, 25th-75th percentiles for a window size of 32 seconds) of 1.5 (0.3-3.3) and 4.0 (1.8-5.5) breaths per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over 90% of the input data are kept). Conclusion: Increased robustness of RR estimation by the proposed method was demonstrated. Significance: This work demonstrates that the use of large publicly available datasets is essential for improving the robustness of wearable-monitoring algorithms for use in clinical practice.
An abnormal respiratory rate is often the earliest sign of critical illness. A reliable estimate of respiratory rate is vital in the application of remote telemonitoring systems, which may facilitate ...early supported discharge from hospital or prompt recognition of physiological deterioration in high-risk patient groups. Traditional approaches use analysis of respiratory sinus arrhythmia from the electrocardiogram (ECG), but this phenomenon is predominantly limited to the young and healthy. Analysis of the photoplethysmogram (PPG) waveform offers an alternative means of non-invasive respiratory rate monitoring, but further development is required to enable reliable estimates. This review conceptualizes the challenge by discussing the effect of respiration on the PPG waveform and the key physiological mechanisms that underpin the derivation of respiratory rate from the PPG.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A ...plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice.
The respiration rate (RR) is one of the physiological signals deserving monitoring for assessing human health and emotional states. However, traditional devices, such as the respiration belt to be ...worn around the chest, are not always a feasible solution (e.g., telemedicine, device discomfort). Recently, novel approaches have been proposed aiming at estimating RR in a less invasive yet reliable way, requiring the acquisition and processing of contact or remote Photoplethysmography (contact reference and remote-PPG, respectively). The aim of this paper is to address the lack of systematic evaluation of proposed methods on publicly available datasets, which currently impedes a fair comparison among them. In particular, we evaluate two prominent families of PPG processing methods estimating Respiratory Induced Variations (RIVs): the first encompasses methods based on the direct extraction of morphological features concerning the RR; and the second group includes methods modeling respiratory artifacts adopting, in the most promising cases, single-channel blind source separation. Extensive experiments have been carried out on the public BP4D+ dataset, showing that the morphological estimation of RIVs is more reliable than those produced by a single-channel blind source separation method (both in contact and remote testing phases), as well as in comparison with a representative state-of-the-art Deep Learning-based approach for remote respiratory information estimation.
Music and the heart Koelsch, Stefan; Jäncke, Lutz
European heart journal,
2015-Nov-21, 2015-11-21, 20151121, Letnik:
36, Številka:
44
Journal Article
Recenzirano
Odprti dostop
Music can powerfully evoke and modulate emotions and moods, along with changes in heart activity, blood pressure (BP), and breathing. Although there is great heterogeneity in methods and quality ...among previous studies on effects of music on the heart, the following findings emerge from the literature: Heart rate (HR) and respiratory rate (RR) are higher in response to exciting music compared with tranquilizing music. During musical frissons (involving shivers and piloerection), both HR and RR increase. Moreover, HR and RR tend to increase in response to music compared with silence, and HR appears to decrease in response to unpleasant music compared with pleasant music. We found no studies that would provide evidence for entrainment of HR to musical beats. Corresponding to the increase in HR, listening to exciting music (compared with tranquilizing music) is associated with a reduction of heart rate variability (HRV), including reductions of both low-frequency and high-frequency power of the HRV. Recent findings also suggest effects of music-evoked emotions on regional activity of the heart, as reflected in electrocardiogram amplitude patterns. In patients with heart disease (similar to other patient groups), music can reduce pain and anxiety, associated with lower HR and lower BP. In general, effects of music on the heart are small, and there is great inhomogeneity among studies with regard to methods, findings, and quality. Therefore, there is urgent need for systematic high-quality research on the effects of music on the heart, and on the beneficial effects of music in clinical settings.
Heart rate (HR) and respiratory rate (
) can be estimated by processing videos framing the upper body and face regions without any physical contact with the subject. This paper proposed a technique ...for continuously monitoring HR and
via a multi-ROI approach based on the spectral analysis of RGB video frames recorded with a mobile device (i.e., a smartphone's camera). The respiratory signal was estimated by the motion of the chest, whereas the cardiac signal was retrieved from the pulsatile activity at the level of right and left cheeks and forehead. Videos were recorded from 18 healthy volunteers in four sessions with different user-camera distances (i.e., 0.5 m and 1.0 m) and illumination conditions (i.e., natural and artificial light). For HR estimation, three approaches were investigated based on single or multi-ROI approaches. A commercially available multiparametric device was used to record reference respiratory signals and electrocardiogram (ECG). The results demonstrated that the multi-ROI approach outperforms the single-ROI approach providing temporal trends of both the vital parameters comparable to those provided by the reference, with a mean absolute error (MAE) consistently below 1 breaths·min
for
in all the scenarios, and a MAE between 0.7 bpm and 6 bpm for HR estimation, whose values increase at higher distances.
Monitoring of heart rate (HR) and respiratory rate ( f R ) is fundamental to assess the health status of an individual. To address this scope, technologies which frame the upper body and the face ...regions of a subject without any physical contact can be used. Motion artifacts can affect the applicability of non-contact methods to the continuous monitoring of these parameters as well as the computational burden. This paper focuses on a technique based on images captured with a single digital camera for the continuous estimation of HR and f R . The main goal is to analyze how velocity of face movements and ROI tracking duration influence the performance of the method. Tests were performed on healthy volunteers during motionless trials (i.e., at rest and after exercise), head and torso movements, and during physical exercise. Results demonstrated that a continuous estimation of HR and f R can be performed with acceptable errors under changing ROI tracking duration and velocity in motionless trials (MAE below 5 bpm and 3.42 breaths∙min -1 for HR and f R , respectively), whereas during movements (mimicking head and torso movements, and during exercise) the error increases (MAE up to 5.42 bpm and up to 5.03 breaths∙min -1 for HR and f R , respectively). The proposed investigation can provide a framework for the continuous estimation of HR and f R during both static and dynamic activities by optimizing the ROI tracking duration under different velocity of face movements.
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