❤️⚡ CARDIO-RS ⚡❤️

A Rust library for Heart Rate Variability (HRV) analysis!

📊 Compute HRV time-domain & frequency-domain & geometric metrics from RR intervals with ease!
💓 Preprocess ECG & PPG raw data for accurate HRV analysis!
📟 Supports embedded systems with no_std!

✨ Features

✅ Time-domain HRV metrics: SDNN, RMSSD, PNN50, SDSD, CVSD, and more!
✅ Frequency-domain HRV metrics: LF, HF, VLF using Welch’s method!
✅ Geometric-domain HRV metrics: Triangular Index & TINN!
✅ ECG & PPG preprocessing: Filtering, denoising, peak detection!
✅ Raw & interpolated RR intervals supported!
✅ 🚀 no_std compatibility!
✅ Window Analysis: Split data into windows of a defined size for segment-based HRV analysis.
✅ Live Analysis: Perform real-time HRV analysis with customizable pipelines for processing RR intervals!

📏 Time-domain HRV Metrics

• 🔹 RMSSD – Measures short-term HRV.
• 🔹 SDNN – Measures overall HRV.
• 🔹 PNN50 – Percentage of successive RR intervals > 50ms.
• 🔹 SDSD – Standard deviation of successive differences.
• 🔹 Mean HR – Average heart rate.
• 🔹 AVNN – Mean of all RR intervals.
• 🔹 CVSD – RMSSD divided by the mean RR interval.

📐 Geometric HRV Metrics

• 📊 Triangular Index – Measures RR interval distribution.
• 📊 TINN (Triangular Interpolation of NN Interval Histogram) – Estimates RR variability.

🎵 Frequency-domain HRV Metrics

📡 Computed using Welch’s method for spectral density estimation:

• 🎼 LF (Low Frequency) – Sympathetic & parasympathetic balance (0.04 - 0.15 Hz).
• 🎼 HF (High Frequency) – Parasympathetic activity (0.15 - 0.40 Hz).
• 🎼 VLF (Very Low Frequency) – Long-term regulatory processes (0.003 - 0.04 Hz).

🏥 ECG & PPG Data Processing

📡 ECG Processing – R-wave peak detection, filtering, and RR extraction!
💡 PPG Processing – Pulse detection & signal cleaning for HRV computation!

🪟 Window Analysis

Cardio-rs supports window analysis, where your data can be split into windows of a defined size. This feature is ideal for segment-based HRV analysis, where the data is divided into distinct time periods (windows).

Example:

use cardio_rs::{
    windows_analysis::WindowsAnalysisBuilder,
};

let data = vec![800., 810., 790., 765., 780., 800., 810., 795., 770., 785.];

// Create the window analysis builder
let analysis = WindowsAnalysisBuilder::new(data)
    .with_window_size(3_000.)
    .build();

// Get the HRV metrics for each window
println!("{:?}", analysis.metrics);

This allows you to perform HRV analysis on distinct segments of data, making it ideal for scenarios where you want to analyze specific time periods independently.

🔄 Live Analysis

The live analysis module enables real-time HRV analysis, processing incoming RR intervals and calculating HRV metrics dynamically. This allows you to process data in real-time as it is received.

You can define a custom analysis pipeline for processing the data, enabling flexible and modular HRV calculations. A default pipeline is provided for standard HRV calculations.

Example:

use cardio_rs::{
    live_analysis::TimeQueue,
    HrvMetrics,
};
use cardio_rs::test_data::RR_INTERVALS;

let mut queue = TimeQueue::new(60_000);
let rr_intervals = RR_INTERVALS.to_vec();
for interval in rr_intervals {
    queue.push(interval);
}

let hrv = queue.get_hrv();
println!("Calculated HRV: {:?}", hrv);

🦀 Example Usage

🚀 Compute HRV Metrics & Load ECG Data

use cardio_rs::{
    standard_analysis,
    HrvMetrics,
};

let path = "tests/ecg.csv";
let signal = "ECG_Raw";
let rate = 1000.;

let hrv_metrics = standard_analysis!(path, signal, rate);
println!("{:?}", hrv_metrics);

#[cfg(feature = "std")] {
use cardio_rs::{
    processing_utils::{RRIntervals, EctopicMethod, DetectOutliers},
    time_domain::TimeMetrics,
    geometric_domain::GeometricMetrics,
    frequency_domain::FrequencyMetrics,
    io_utils::{DataBuilder},
    standard_analysis,
    HrvMetrics,
};

let path = "tests/ecg.csv";
let signal = "ECG_Raw";
let time = "Time";
let data = DataBuilder::new(path.into(), signal.into())
    .with_time(time.into())
    .build()
    .unwrap();

let hrv_metrics = standard_analysis!(data.clone().get_rr());
println!("{:?}", hrv_metrics);

// Or manually for more control

let mut rr_intervals = RRIntervals::new(data.get_rr());

rr_intervals.detect_ectopics(EctopicMethod::Karlsson);
rr_intervals.detect_outliers(&300., &2_000.);
rr_intervals.remove_outliers_ectopics();

let time_metrics = TimeMetrics::compute(rr_intervals.as_slice());
println!("{:?}", time_metrics);

let frequency_metrics = FrequencyMetrics::compute(rr_intervals.as_slice(), 10.);
println!("{:?}", frequency_metrics);

let geo_metrics = GeometricMetrics::compute(rr_intervals.as_slice());
println!("{:?}", geo_metrics);
}

🔥 Start analyzing HRV today with cardio-rs! 🚀💓