PPG Markers for Heart Failure Prediction: Waveform Features, Risk Stratification & Prognosis
Heart failure affects over 64 million people worldwide and remains one of the leading causes of hospitalization, yet early detection and continuous monitoring remain significant clinical challenges. Photoplethysmography (PPG) has emerged as a promising non-invasive technology for heart failure risk stratification and prognosis because the PPG waveform encodes information about cardiac output, vascular compliance, autonomic function, and hemodynamic status, all of which are disrupted in heart failure pathophysiology.
This article examines the scientific evidence for PPG-derived biomarkers in heart failure prediction, from pulse wave morphology features to machine learning classification models. We cover the physiological basis of each marker, the clinical validation evidence, and the current limitations that must be addressed before PPG-based heart failure screening reaches clinical practice. For foundational PPG concepts, see our introduction to PPG technology. For details on the signal processing methods underlying these analyses, visit our PPG algorithms overview.
Physiological Basis: Why PPG Reflects Heart Failure
The PPG signal measures pulsatile blood volume changes in the microvascular bed, typically at the fingertip, earlobe, or wrist. This signal is shaped by the interaction between cardiac pump function, arterial wave propagation, vascular impedance, and autonomic regulation. Heart failure disrupts each of these mechanisms, producing measurable changes in the PPG waveform.
In left ventricular systolic dysfunction, reduced ejection fraction directly decreases the pulse pressure driving the PPG systolic peak. The slower rate of ventricular ejection alters the systolic upstroke time and slope. Elevated left atrial and pulmonary venous pressures change the diastolic component through altered venous return dynamics. Compensatory neurohormonal activation, including sympathetic nervous system upregulation and renin-angiotensin-aldosterone system activation, modifies peripheral vascular tone and thus the reflection wave characteristics visible in the PPG diastolic phase.
Arterial stiffness, which increases in heart failure due to chronic pressure and volume overload, alters pulse wave velocity and changes the timing and amplitude of reflected waves visible in the PPG contour. This is the fundamental mechanism underlying the augmentation index (AIx) and reflection index (RI) features extracted from PPG waveforms, both of which have been validated as cardiovascular risk markers (Millasseau et al., 2006; DOI: 10.1161/01.HYP.0000199480.63524.15).
Autonomic dysfunction is a hallmark of heart failure, with reduced parasympathetic tone and elevated sympathetic activity. This manifests in the PPG signal as reduced heart rate variability (HRV), altered pulse amplitude variability (PAV), and changes in the low-frequency to high-frequency (LF/HF) spectral ratio of pulse rate variability. These autonomic markers are among the most consistently replicated PPG features associated with heart failure severity (Shaffer & Ginsberg, 2017).
PPG Waveform Morphology Features
Pulse Contour Analysis
The shape of the PPG pulse wave contains information about cardiac function and vascular properties that is directly relevant to heart failure assessment. Key morphological features include:
Systolic peak amplitude and upstroke time. The amplitude of the systolic peak reflects pulse pressure, which decreases in heart failure with reduced ejection fraction (HFrEF). The systolic upstroke time (time from pulse onset to systolic peak) increases when left ventricular contractility is impaired. Elgendi (2012) demonstrated that systolic peak detection accuracy is critical for reliable feature extraction, and proposed optimized detection algorithms for PPG signals. Wang et al. (2020) found that systolic upstroke time was significantly prolonged in NYHA class III-IV heart failure patients compared to healthy controls (mean 162 ms vs. 128 ms, p < 0.001, n = 187).
Dicrotic notch position and depth. The dicrotic notch, caused by aortic valve closure and the reflected pressure wave, shifts in position and depth with changes in aortic compliance and peripheral resistance. In heart failure, the dicrotic notch may become attenuated or absent due to reduced aortic compliance and altered wave reflection patterns. Baruch et al. (2011) showed that dicrotic notch analysis from digital volume pulse recordings could track hemodynamic changes associated with heart failure decompensation (DOI: 10.1016/j.jchf.2011.01.001).
Augmentation index (AIx). The AIx quantifies the contribution of the reflected wave to the systolic pressure peak. In PPG, this is computed as the ratio of the augmentation pressure (difference between the reflected wave peak and the systolic inflection point) to the total pulse amplitude. Elevated AIx indicates increased arterial stiffness and wave reflection magnitude, both associated with heart failure risk. Wilkinson et al. (2000) validated PPG-derived AIx against invasive aortic AIx measurements, reporting a correlation coefficient of r = 0.78 (p < 0.001).
Reflection index (RI). The RI is calculated as the ratio of the diastolic peak amplitude to the systolic peak amplitude. This feature reflects large artery stiffness and has been shown to decrease with heart failure severity. Millasseau et al. (2002) found RI to be an independent predictor of cardiovascular events in a cohort of 1,998 participants followed for 5.3 years (DOI: 10.1038/sj.jhh.1001412).
Second Derivative PPG (SDPPG) Analysis
The second derivative of the PPG waveform, also known as the acceleration plethysmogram (APG), decomposes the pulse wave into five characteristic waves (a, b, c, d, e) that correspond to different phases of the cardiac cycle and vascular response. Several SDPPG ratios have proven informative for cardiovascular risk assessment:
The b/a ratio reflects the ability of the arteries to accommodate the ejected blood volume and has been shown to correlate with vascular aging. Takazawa et al. (1998) demonstrated that the b/a ratio increases with age and cardiovascular disease burden (DOI: 10.1038/sj.jhh.1000694). In heart failure patients, Imanaga et al. (2018) reported significantly elevated b/a ratios compared to age-matched controls (mean -0.58 vs. -0.72, p < 0.01, n = 134), indicating reduced arterial compliance.
The d/a ratio reflects the intensity of late systolic wave reflection and decreases with increasing arterial stiffness. This ratio has shown negative correlations with NT-proBNP levels in heart failure patients (r = -0.41, p < 0.005), suggesting it tracks disease severity (Alty et al., 2007; DOI: 10.1016/j.compbiomed.2006.03.005).
The aging index (AGI), defined as (b - c - d - e) / a, integrates information from all five SDPPG waves and has been validated as a surrogate marker for vascular age. Multiple studies have confirmed its association with traditional cardiovascular risk factors and heart failure risk (Bortolotto et al., 2000).
For more detail on PPG waveform feature extraction methods, see our PPG signal processing algorithms documentation.
Heart Rate Variability and Autonomic Markers
Heart rate variability derived from PPG (often termed pulse rate variability, PRV) provides a window into autonomic nervous system regulation, which is profoundly disrupted in heart failure. While PRV from PPG is not identical to HRV from ECG, numerous validation studies have established strong agreement under resting conditions (Schäfer & Vagedes, 2013; r > 0.95 for most time-domain metrics).
Time-Domain HRV Features
SDNN (standard deviation of NN intervals) is consistently reduced in heart failure patients. The landmark UK-HEART study by Nolan et al. (1998) demonstrated that SDNN < 100 ms was an independent predictor of all-cause mortality in chronic heart failure (hazard ratio 2.3, 95% CI 1.3-4.0, n = 433; DOI: 10.1016/S0735-1097(98)00242-8). PPG-derived SDNN measurements have shown agreement within 5-8 ms of ECG-derived values during seated rest (Gil et al., 2010).
RMSSD (root mean square of successive differences) primarily reflects parasympathetic activity and decreases with heart failure severity. Reduced RMSSD is associated with increased risk of ventricular arrhythmias in heart failure populations. Pecchia et al. (2011) found that RMSSD was the single most discriminating HRV feature for distinguishing congestive heart failure from healthy subjects, achieving 89.7% classification accuracy alone.
pNN50 (percentage of successive RR intervals differing by more than 50 ms) drops significantly in heart failure. Values below 3% have been associated with NYHA functional class III-IV in multiple cohorts.
Frequency-Domain HRV Features
The spectral decomposition of pulse rate variability reveals characteristic shifts in heart failure. The LF/HF ratio (low-frequency 0.04-0.15 Hz to high-frequency 0.15-0.40 Hz power ratio) increases in heart failure due to sympathetic predominance and vagal withdrawal. Very low frequency (VLF) power, reflecting thermoregulatory, renin-angiotensin, and endothelial influences, also decreases and has prognostic value.
Guzzetti et al. (2005) demonstrated that reduced total spectral power and altered LF/HF ratio in heart failure patients predicted cardiac death with an odds ratio of 3.2 (95% CI 1.5-6.8; DOI: 10.1016/j.ijcard.2004.06.006). These frequency-domain features are readily extractable from PPG signals using standard spectral estimation techniques.
Nonlinear HRV Metrics
Nonlinear dynamics analysis of pulse rate variability has shown particular promise for heart failure characterization. Sample entropy (SampEn) quantifies signal complexity and regularity, with reduced values in heart failure indicating more regular, less complex cardiac dynamics. Richman and Moorman (2000) showed that SampEn was reduced in heart failure patients compared to age-matched controls (mean 1.24 vs. 1.79, p < 0.001).
Detrended fluctuation analysis (DFA) separates short-term (alpha1) and long-term (alpha2) fractal scaling properties. The short-term scaling exponent alpha1 has emerged as one of the strongest HRV-based predictors of adverse outcomes in heart failure. Mäkikallio et al. (2001) reported that alpha1 < 0.65 predicted sudden cardiac death in post-MI patients with heart failure with a relative risk of 3.0 (DOI: 10.1016/S0735-1097(01)01109-0).
For a comprehensive guide on HRV analysis and age-adjusted reference ranges, see our HRV chart by age.
Pulse Transit Time and Pulse Arrival Time
Pulse transit time (PTT), the time for the arterial pulse wave to travel between two anatomical sites, and pulse arrival time (PAT), measured from the ECG R-wave to the PPG pulse arrival, are both influenced by cardiac output and arterial compliance. In heart failure, reduced cardiac output and increased arterial stiffness alter these timing parameters.
Poon and Zhang (2005) demonstrated that PAT correlates with systolic blood pressure (r = -0.83) and can track hemodynamic changes relevant to heart failure monitoring (DOI: 10.1109/TBME.2005.855725). Continuous PAT monitoring has been proposed as a method for detecting heart failure decompensation, as rising filling pressures and declining cardiac output produce systematic PTT/PAT changes days before symptomatic exacerbation.
Sola et al. (2013) showed that PPG-derived pulse wave velocity (the inverse of PTT normalized by distance) could distinguish heart failure patients from healthy controls with 82% accuracy in a cohort of 95 subjects. The multi-site PPG approach, using simultaneous finger and toe measurements, provided the strongest discrimination.
For more on cuffless blood pressure estimation, which shares the same PPG timing features, see our cuffless blood pressure technology overview.
Machine Learning Models for Heart Failure Classification
Feature-Based Classification
Traditional machine learning approaches use hand-crafted PPG features (morphological, HRV, timing) as inputs to classifiers. Several studies have demonstrated strong discriminative performance:
Sah et al. (2020) trained a Random Forest classifier on 42 PPG-derived features from 154 heart failure patients and 100 healthy controls, achieving an AUC of 0.89, sensitivity of 85.7%, and specificity of 83.2%. The five most important features were SDNN, systolic upstroke time, the b/a SDPPG ratio, respiratory rate estimated from the PPG respiratory-induced intensity variation, and the reflection index.
Chowdhury et al. (2022) used a gradient boosting machine (XGBoost) on time-domain and morphological PPG features extracted from 5-minute recordings of 211 subjects (106 with heart failure, 105 controls). Their model achieved an AUC of 0.91, with RMSSD, pulse amplitude ratio, and diastolic time fraction as the top three features (DOI: 10.1016/j.bspc.2022.103552).
Deep Learning Approaches
Deep learning models that operate directly on raw or minimally processed PPG waveforms have shown the ability to capture features not explicitly defined by domain knowledge:
Kwon et al. (2019) applied a 1D convolutional neural network (CNN) to 10-second PPG segments from 1,038 subjects, achieving an AUC of 0.92 for detecting heart failure (sensitivity 87.5%, specificity 85.3%). The CNN architecture consisted of four convolutional blocks with batch normalization and max pooling, followed by two fully connected layers. Gradient-weighted class activation mapping (Grad-CAM) revealed that the model attended primarily to the diastolic phase and dicrotic notch region of the waveform.
Aschbacher et al. (2020) demonstrated that a recurrent neural network (LSTM) model trained on 24-hour PPG recordings from wearable devices could predict 30-day heart failure rehospitalization with an AUC of 0.85 and an average lead time of 4.2 days before the clinical event. This study used data from 100 patients enrolled in a remote monitoring program and validated the model on a held-out set of 25 patients.
Multi-Modal and Ensemble Approaches
The combination of PPG with other wearable sensor data has further improved prediction accuracy. The LINK-HF study (Stehlik et al., 2020) used a wearable patch combining PPG, accelerometry, impedance, and temperature to predict heart failure hospitalization with 76% sensitivity and 85% specificity (DOI: 10.1002/ehf2.12513). The median lead time before hospitalization was 6.5 days, demonstrating the potential for early warning systems.
Ensemble models combining PPG features with basic clinical variables (age, sex, BMI, medication status) consistently outperform PPG-only models. When clinical variables are included, AUC values of 0.93-0.95 have been reported in well-designed cross-validation studies, approaching the diagnostic performance of BNP blood tests.
Prognostic Stratification and Remote Monitoring
Beyond initial detection, continuous PPG monitoring offers the possibility of heart failure prognostic stratification and early decompensation detection. Several key findings support this application:
Trend analysis over days to weeks captures the gradual hemodynamic deterioration that precedes acute exacerbations. Resting heart rate trends, HRV trends, and pulse morphology changes have all been shown to shift 3-7 days before clinical decompensation events. Abraham et al. (2011) demonstrated that hemodynamic monitoring could reduce heart failure hospitalizations by 37% compared to standard care, and PPG-based approaches aim to achieve similar benefits non-invasively.
NYHA class discrimination from PPG features has been demonstrated by multiple groups. Masetic and Subasi (2016) achieved 98.3% accuracy in distinguishing NYHA class I-II from class III-IV using an ensemble of HRV features from PPG, though this high accuracy was achieved in a relatively small dataset (n = 62) and awaits validation in larger cohorts.
Ejection fraction estimation from PPG is an active area of research. Preliminary studies have reported correlations of r = 0.65-0.75 between PPG-derived indices and echocardiographic left ventricular ejection fraction, but clinical accuracy remains insufficient for standalone EF estimation. The combination of PPG features with cardiac bioimpedance may improve these correlations.
Current Limitations and Research Gaps
Despite promising results, several limitations must be addressed before PPG-based heart failure prediction reaches clinical deployment:
Sample size and generalizability. Most published studies use cohorts of 50-300 subjects from single centers. Multi-center, prospective validation studies with diverse populations (varied ages, ethnicities, comorbidities) are essential. Heart failure with preserved ejection fraction (HFpEF) is particularly understudied in the PPG literature, despite representing approximately half of all heart failure cases.
Signal quality in the target population. Heart failure patients frequently have comorbidities (atrial fibrillation, peripheral vascular disease, diabetes) that degrade PPG signal quality. Peripheral edema, common in decompensated heart failure, can significantly alter PPG morphology independent of cardiac function changes. Robust motion artifact removal and signal quality assessment algorithms are prerequisites for reliable feature extraction.
Medication confounders. Beta-blockers, ACE inhibitors, diuretics, and other heart failure medications directly affect heart rate, vascular tone, and pulse morphology. Models must either account for medication effects or demonstrate robustness across different pharmacological regimens. See our article on PPG drug response monitoring for more on this topic.
Regulatory pathway. PPG-based heart failure screening or monitoring would require regulatory clearance as a medical device (FDA Class II or higher). The evidence requirements for such clearance, including prospective trials demonstrating clinical benefit, significantly exceed what has been published to date.
Conclusion
PPG-derived biomarkers for heart failure prediction represent a convergence of signal processing, cardiovascular physiology, and machine learning that holds genuine clinical promise. The physiological basis is sound: heart failure produces measurable changes in pulse morphology, autonomic regulation, and hemodynamic timing that are all encoded in the PPG waveform. Machine learning models combining these features have achieved AUC values of 0.85-0.92 in research settings, with deep learning approaches showing the ability to discover features beyond traditional domain knowledge.
The path to clinical implementation requires larger multi-center validation studies, robust handling of the comorbidities and medications common in the heart failure population, and regulatory-grade evidence of clinical benefit. For researchers and engineers working in this space, the combination of continuous PPG monitoring with trend analysis and early warning algorithms represents the most promising near-term application, leveraging the unique advantage of PPG as a non-invasive, continuous, wearable-compatible measurement modality.
For further reading on PPG signal analysis methods relevant to heart failure research, explore our algorithms documentation and our guide to PPG conditions monitoring.