Can PPG be used for HRV analysis?

Heart rate variability (HRV) represents one of the most promising markers of the autonomic nervous system (ANS) regulation. However, it requires the acquisition of the ECG signal in order to reliably detect the RR intervals, which is not always easily and comfortably available in personal health applications. Additionally, due to progress in single spot optical sensors, photoplethysmography (PPG) is an interesting alternative for heartbeat interval measurements, since it is a more convenient and a less intrusive measurement technique. Driven by the technological advances in such sensors, wrist-worn devices are becoming a commodity, and the interest in the assessment of HRV indexes from the PPG analysis (pulse rate variability - PRV) is rising. In this study, we investigate the hypothesis of using PRV features as surrogates for HRV indexes, in three different contexts: healthy subjects at rest, healthy subjects after physical exercise and subjects with cardiovascular diseases (CVD). Additionally, we also evaluate which are the characteristic points better suited for PRV analysis in these contexts, i.e. the PPG waveform characteristic points leading to the PRV features that present the best estimates of HRV (correlation and error analysis). The achieved results suggest that the PRV can be often used as an alternative for HRV analysis in healthy subjects, with significant correlations above 82%, for both time and frequency features. Contrarily, in the post-exercise and CVD subjects, time and (most importantly) frequency domain features shall be used with caution (mean correlations ranging from 68% to 88%).

Assessment of cardiovascular function from multi-Gaussian fitting of a finger photoplethysmogram.

Monitoring of cardiovascular function on a beat-to-beat basis is fundamental for protecting patients in different settings including emergency medicine and interventional cardiology, but still faces technical challenges and several limitations. In the present study, we propose a new method for the extraction of cardiovascular performance surrogates from analysis of the photoplethysmographic (PPG) signal alone.We propose using a multi-Gaussian (MG) model consisting of five Gaussian functions to decompose the PPG pulses into its main physiological components. From the analysis of these components, we aim to extract estimators of the left ventricular ejection time, blood pressure and vascular tone changes. Using a multi-derivative analysis of the components related with the systolic ejection, we investigate which are the characteristic points that best define the left ventricular ejection time (LVET). Six LVET estimates were compared with the echocardiographic LVET in a database comprising 68 healthy and cardiovascular diseased volunteers. The best LVET estimate achieved a low absolute error (15.41 ± 13.66 ms), and a high correlation (ρ = 0.78) with the echocardiographic reference.To assess the potential use of the temporal and morphological characteristics of the proposed MG model components as surrogates for blood pressure and vascular tone, six parameters have been investigated: the stiffness index (SI), the T1_d and T1_2 (defined as the time span between the MG model forward and reflected waves), the reflection index (RI), the R1_d and the R1_2 (defined as their amplitude ratio). Their association to reference values of blood pressure and total peripheral resistance was investigated in 43 volunteers exhibiting hemodynamic instability. A good correlation was found between the majority of the extracted and reference parameters, with an exception to R1_2 (amplitude ratio between the main forward wave and the first reflection wave), which correlated low with all the reference parameters. The highest correlation ([Formula: see text] = 0.45) was found between T1_2 and the total peripheral resistance index (TPRI); while in the patients that experienced syncope, the highest agreement ([Formula: see text] = 0.57) was found between SI and systolic blood pressure (SBP) and mean blood pressure (MBP).In conclusion, the presented method for the extraction of surrogates of cardiovascular performance might improve patient monitoring and warrants further investigation.

Multi-channel audio-based estimation of the Pre-Ejection Period.

Systolic time intervals (STI) have significant diagnostic and prognostic value to assess the global cardiac function. Presently, STIs are regarded as a promising tool for long-term follow-up of patients with chronic cardiovascular diseases. Heart sound has proven to be a valuable approach for STI estimation, in particular for the Pre-Ejection Period (PEP). However, since the optimal auscultation site varies from individual to individual, as well as with the position of the body, its application in single-channel and fixed auscultation site setups poses practical difficulties. Hence, we extend our previous work on PEP estimation to a multi-channel sound acquisition setup, where signal redundancy is exploited. A channel selection method is proposed and the best channel is selected for PEP estimation. As a preliminary study, the devised algorithms were evaluated with respect to echocardiography reference on a set of 236 heartbeats collected from 8 healthy subjects in two sound auscultation sites. The channel selection approach led to 8.4% estimation error decrease, in comparison to a single-channel approach. Current results support our assumption that a multi-channel audio-based strategy can be applied to assess STI in personal health application scenarios.

Beat-to-beat systolic time-interval measurement from heart sounds and ECG.

Systolic time intervals are highly correlated to fundamental cardiac functions. Several studies have shown that these measurements have significant diagnostic and prognostic value in heart failure condition and are adequate for long-term patient follow-up and disease management. In this paper, we investigate the feasibility of using heart sound (HS) to accurately measure the opening and closing moments of the aortic heart valve. These moments are crucial to define the main systolic timings of the heart cycle, i.e. pre-ejection period (PEP) and left ventricular ejection time (LVET). We introduce an algorithm for automatic extraction of PEP and LVET using HS and electrocardiogram. PEP is estimated with a Bayesian approach using the signal's instantaneous amplitude and patient-specific time intervals between atrio-ventricular valve closure and aortic valve opening. As for LVET, since the aortic valve closure corresponds to the start of the S2 HS component, we base LVET estimation on the detection of the S2 onset. A comparative assessment of the main systolic time intervals is performed using synchronous signal acquisitions of the current gold standard in cardiac time-interval measurement, i.e. echocardiography, and HS. The algorithms were evaluated on a healthy population, as well as on a group of subjects with different cardiovascular diseases (CVD). In the healthy group, from a set of 942 heartbeats, the proposed algorithm achieved 7.66 ± 5.92 ms absolute PEP estimation error. For LVET, the absolute estimation error was 11.39 ± 8.98 ms. For the CVD population, 404 beats were used, leading to 11.86 ± 8.30 and 17.51 ± 17.21 ms absolute PEP and LVET errors, respectively. The results achieved in this study suggest that HS can be used to accurately estimate LVET and PEP.

Multi-Gaussian fitting for the assessment of left ventricular ejection time from the photoplethysmogram.

The Left ventricular ejection time (LVET) is one of the primary surrogates of the left ventricular contractility and stroke volume. Its continuous monitoring is considered to be a valuable hypovolumia prognostic parameter and an important risk predictor in cardiovascular diseases such as cardiac and light chain amyloidosis. In this paper, we present a novel methodology for the assessment of LVET based the Photoplethysmographic (PPG) waveform. We propose the use of Gaussian functions to model both systolic and diastolic phases of the PPG beat and consequently determine the onset and offset of the systolic ejection from the analysis of the systolic phase 3(rd) derivative. The results achieved by the proposed methodology were compared with the algorithm proposed by Chan et al. [1], revealing better estimation of LVET (15.84 ± 13.56 ms vs 23.01 ± 14.60 ms), and similar correlation with the echocardiographic reference (0.73 vs 0.75).

Pollutant removal in a multi-stage municipal wastewater treatment system comprised of constructed wetlands and a maturation pond, in a temperate climate.

A multi-stage municipal wastewater treatment system is proposed to comply with Mexican standards for discharge into receiving water bodies. The system is located in Santa Fe de la Laguna, Mexico, an area with a temperate climate. It was designed for 2,700 people equivalent (259.2 m3/d) and consists of a preliminary treatment, a septic tank as well as two modules operating in parallel, each consisting of a horizontal subsurface-flow wetland, a maturation pond and a vertical flow polishing wetland. After two years of operation, on-site research was performed. An efficient biochemical oxygen demand (BOD5) (94-98%), chemical oxygen demand (91-93%), total suspended solids (93-97%), total Kjeldahl nitrogen (56-88%) and fecal coliform (4-5 logs) removal was obtained. Significant phosphorus removal was not accomplished in this study (25-52%). Evapotranspiration was measured in different treatment units. This study demonstrates that during the dry season wastewater treatment by this multi-stage system cannot comply with the limits established by Mexican standards for receiving water bodies type 'C'. However, it has demonstrated the system's potential for less restrictive uses such as agricultural irrigation, recreation and provides the opportunity for wastewater treatment in rural areas without electric energy.

Beat-to-beat cardiac output inference using heart sounds.

Cardiac output (CO) change is the primary compensatory mechanism that responds to oxygenation demand. Its continuous monitoring has great potential for the diagnosis and management of cardiovascular diseases, both in hospital as well as in ambulatory settings. However, CO measurements are currently limited to hospital settings only. In this paper, we present an extension of the model proposed by Finkelstein for beat-to-beat CO assessment. We use a nonlinear model consisting of a two-layer feed-forward artificial neural network. In addition to demographic (body surface area and age) and physiological parameters (HR), surrogates of contractility, afterload and mean arterial pressure based on systolic time intervals (STIs), estimated from echocardiography and heart sounds are used as inputs to our models. The results showed that the proposed models--with echocardiography as reference--produce better estimations of stroke volume/CO than the Finkelstein model (12.83 ± 10.66 ml vs 7.23 ± 6.6 ml), as well as higher correlation (0.46 vs 0.82).

Comparison of systolic time interval measurement modalities for portable devices.

Systolic time intervals (STI) have shown significant diagnostic and prognostic value to assess the global cardiac function. Their value has been largely established in hospital settings. Currently, STI are considered a promising tool for long-term patient follow-up with chronic cardiovascular diseases. Several technologies exist that enable beat-by-beat assessment of STI in personal health application scenarios. A comparative study is presented using the echocardiographic gold standard synchronized with impedance cardiography (ICG), phonocardiography (PCG) and photoplethysmography (PPG). The ability of these competing technologies in assessing the pre ejection period (PEP) and the left ventricle ejection time (LVET) is given a general overview with comparative results.

Assessing systolic time-intervals from heart sound: a feasibility study.

Systolic time intervals are highly correlated to fundamental cardiac functions. In this paper we investigate the feasibility of using heart sound (HS) to accurately measure the opening and closing moments of the aortic valve, since these are crucial moments to define the main systolic timings of the heart cycle, i.e. the pre-ejection period (PEP) and the left ventricular ejection time (LVET). We introduce a HS model, which is applied to define several features that provide clear markers to identify these moments in the HS. Using these features and a comparative analysis with registered echocardiographies from 17 subjects, the results achieved in this study suggest that HS can be used to accurately estimate LVET and PEP.

Heavy metal concentrations in water and bottom sediments of a Mexican reservoir.

Metal concentrations have been measured in water and sediments of the Jose Antonio Alzate Reservoir, Mexico, using EDXRF and ICP techniques. Spatial and temporal distributions of total metal levels have been identified; no organised pattern was detected for any particular metal concentration. Temporal variations of metal concentrations show evidence of the water self-cleaning capacity of the reservoir, despite the high-level metal contamination determined.