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Early and low cost detection of Heart Failure

Heart Failure is a debilitating condition that most old people encounters. A PoC uses coded signals, Doppler and a sound ML classifier.

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Heart failure (HF) is a complex heart syndrome. It prevents the heart from fulfilling the circulatory demands of the body. A patient is characterized by breathlessness, ankle swelling and fatigue. Medicine doctors formulated several criteria to determine the presence of HF, however there is no remedy.

We propose to use a point of care device, composed of a low cost Doppler device with a trained classifier ,to detect suspicion of HF in primary care.

In order to detect HF with a ML classifier, we train it to recognize features in sounds from the Physionet Cinc or similar competitions.

In our case, signals acquired with a low cost fetal Doppler, are connected to a Linux box hosting the classifier and user GUI. It records heart beats without needs of any gel. We will later code the Doppler signal to obtain more features, specially about fibrous tissues. In a further iteration we will implement the ML classifier on a low cost controller.

Short list of the features:

* This device can measure the acoustic impedance of heart and lung tissues even in non medical environment, no need to move to a medical center.

* This device measures the acoustic impedance of tissues by a home carer, without need for medical staff.

* This device mitigate issues due to unanticipated skin colors, sweating or medical conditions, as it can "phone home" if it found anomalous results. Conversely it receives updates automatically, so what is learn from a single case, is send to every devices at their next update.

* It is very low cost: Certainly less than $150 in its final version.

* It can be used for a long time without creating skin problems, as it uses no gel and is easy to clean.

Actually ultrasound specialists also use such artifacts to detect cysts, tumors, calcifications. B-line is an artifact that is used in ultrasound imaging (or lack of!) to infer medical conditions.

Acoustic impedance:

It is well known that the density of degenerated tissues is lower than those of normal tissues. This is due both to intracellular and extracellular damages.

While using machine learning to detect heart failure is not new, using ML features symptomatic of fibrous tissues is entirely new.

Based on the acoustic impedance the tissue could be classified as: normal, degenerated, granulated and fibrous. Each category indicates specific problems mostly in connective tissues.

Changes in tissues acoustic impedance alone, do not mean in themselves that the medical condition changed. What makes it accurate is that this device can recognize signatures of degenerated tissue thanks to modern statistical technologies such as Hidden Markov Chains.

Sourcing the Transducer

A critical component of this system is the ultrasound transducer. A piezoelectric transducer element transmits energy into the body and receive the resulting reflections.

While we use in our PoC a commercial 3 MHz ultrasound probe, it is possible to build one. The DGH 6000 Scanmate A transducer consists of a single piezo - ceramic element that runs at a center frequency of 10.0 MHz nominal.

Please have a look at this excellent project for suggestions of bill of materials:
https://hackaday.io/project/9281-murgen-open-source-ultrasound-imaging

High-Voltage Transmitters (Pulser)

We need to transmit two frequencies for “0” and “1”, because we code our transmission with pulse’s ID, time’ ID and ECC (maybe Golay code).
We transmit those information in order to gather impedance information.

High-voltage pulsers quickly switch the transducer element to the appropriate programmable high-voltage supplies to generate the transmit waveform.

To generate a simple bipolar transmit waveform, a transmit pulser alternately connects the element to a positive and negative transmit supply voltage

  • 1 × 3 MHz low cost ultrasound probe It costs only $50, but indeed it does not offer beam forming!
  • 1 × A ultrasound pulser This is done with transistors and other discrete components!
  • 1 × Analog front end It uses ordinary operational amplifiers
  • 1 × ML classifier To detect features in the training set, I used a modified subset of jAudio. The training set comes from Physionet, but there are other freely accessible on the net. The classifier is a HMM derived from a speech processing project. I want to use a HMM because it is able to predit states and offer an explanation of the model (the probability of transitions between states)

  • Start of the project

    Jean Pierre Le Rouzic03/21/2017 at 20:31 0 comments

    What I did until now is summarized here:
    I bought a fetal Doppler from Amazon and tested it on myself.

    I found there is no need for gel, and that it is quite easy to find my own heart. It is probably much more difficult to find the heart of a baby in the womb. Future mothers must be warned that it is not a magic tool, my heart is probably one hundred times bigger than a fetus heart. In retrospect those fetal Doppler are impressive!

    I acquire my heart sounds and transfered them on a Linux box. There I studied their spectrum and other characteristics.

    Next I studied the Physionet Cinc 2016 challenge in order to train their most successful software on my heart sounds.

    Alas I do not have bought the Matlab libraries that are needed and an attempt to use Octave (open source alternative) gives no satisfactory outcome.

    Anyway as my goal is to implement this on a low cost processor of the Arm family or even less powerful, Matlab/Octave was out of question.

    As I can program in Java I wrote my own program, which mimics the winning physionel 2016 code. I have great confidence that I can translate this Java code in machine language (I used to work on a small Java machine long ago). And the Java code could be a reference implementation.

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  • 1

    At the moment all you need is to buy a Fetal Doppler ($70) and connect it to a Linux box, where you will have downloaded, both the Physionet 2016 training set and the winning Matlab code (and associated libraries) by Christhian Potes and Saman Parvaneh.

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