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--- a/README.md +++ b/README.md @@ -1,92 +1,89 @@ -# OMNI (Open Source Monitoring of Neonates and Infants) - -<p align="center"> - <image src = 'images/omni-logo.png' > -</p> - - - -## Software Requirements -Run `sh requirements.sh` in a virtual environment in order to download the required libraries. - -## Hardware Requirements (Edge implementation) - - -### Components needed: - - 1. [OpenBCI Ganglion kit](https://shop.openbci.com/products/ganglion-board?variant=13461804483) - - Open source biosignal acquisition hardware for [research grade biosignal acquisition](https://openbci.com/community/published-research-with-openbci/) - - 2. [Raspberry Pi 4](https://www.raspberrypi.org/products/raspberry-pi-4-model-b/) - 3. [Rasberry Pi 4 cooling case](https://www.newegg.com/p/1W8-00Y1-00032) - 4. Shirt using [conductive textile electrodes](https://www.alibaba.com/product-detail/Conductive-textile-ecg-Electrodes_1127697682.html) [1$ for 1 electrode] - - -Wearable ECG electrodes --> OpenBCI Ganglion ---(Bluetooth)---> Raspberry Pi 4 - -## System Configuration -* Ubuntu 16.04 -* Nvidia 1080Ti - (Required for training the model) - -## Train a model to extract R peaks and Heart Rate from ECG waveform. -* Download ECG MITDB monitoring data from https://storage.googleapis.com/mitdb-1.0.0.physionet.org/mit-bih-arrhythmia-database-1.0.0.zip and unzip it. -* To train: `python train.py --preprocess_data --data_path "PATH TO DATA"` - -## Train a model to extract Breathing Rate from ECG waveform. -* Download ECG from the BIDMC database which is derived from MIMIC-II from https://physionet.org/static/published-projects/bidmc/bidmc-ppg-and-respiration-dataset-1.0.0.zip and unzip it. -* To train: `python train.py --preprocess_data --data_path "PATH TO DATA"` - -## Model Inference -* Download ECG from the preterm infant database from https://physionet.org/static/published-projects/picsdb/preterm-infant-cardio-respiratory-signals-database-1.0.0.zip and unzip it. -* Download the Heart Rate computation model from here: https://drive.google.com/open?id=1yI7G4nofjuzFWkD1CfsOtLZxaukTu0di -* Download the Breathing Rate computation model from here: https://drive.google.com/open?id=1ycV74LfGmgcGmLlrPn2VileeFNsGrRZT -* To run inference and view GUI type: `python run_model.py --path_dir "PATH TO DATA" --saved_hr_model_path "PATH TO HR MODEL" --saved_br_model_path "PATH TO BR MODEL" --patient_no 3 --viewer 1` - -# OMNI OpenBCI Pi Inference - - Edge inference of ECG R-peak detection and Respiration extraction using Raspberry Pi 4 using ECG (OpenBCI Ganglion). - - -## Hardware Design - -Wearable ECG electrodes --> OpenBCI Ganglion ---(Bluetooth)---> Raspberry Pi 4 - -## Software Design - -OpenBCI client ----(LSL)---> Python -> PyTorch inference --> Breathing Rate, Heart Rate - - -## Installation instruction - -### Install PyTorch on Raspberry Pi 4: - - 1. Install PyTorch dependicies - `sudo apt install libopenblas-dev libblas-dev m4 cmake cython python3-yaml libatlas-base-dev` - 2. Increase swap file memory to 1600, Edit variable `CONF_SWAPSIZE` in `/etc/dphys-swapfile` - 3. Reset environmental variables like ONNX_ML [Instructions](https://gist.github.com/akaanirban/621e63237e63bb169126b537d7a1d979) - 4. Download PyTorch package compiled for Armv7 ([torch-1.1.0-cp37-cp37m-linux_armv7l.whl](https://github.com/marcusvlc/pytorch-on-rpi/blob/master/torch-1.1.0-cp37-cp37m-linux_armv7l.whl)) - 5. Install using the command `sudo pip3 install torch-1.1.0-cp37-cp37m-linux_armv7l.whl` in the same directory - -Refer [here](https://github.com/marcusvlc/pytorch-on-rpi) for troubleshooting - -### Install OpenBCI Ganglion client on Raspberry Pi 4: - -1. Clone OpenBCI_Python repo - `git clone htps://github.com/OpenBCI/OpenBCI_Python.git` -2. Install the following requisites python packages using `pip3 install` - pylsl, python-osc, six, socketIO-client, websocket-client, Yapsy, xmldict, bluepy -3. Open folder OpenBCI_Python and run - `sudo python3 user.py --board ganglion -a streamer_lsl` to open a lab streaming layer stream of sensor data from the ganglion - -## Instructions to run to perform real time breathing rate/ heart rate inference using OpenBCI data -1. Run the lsl streamer script to get data to the inference script -`sudo python3 user.py --board ganglion -a streamer_lsl` -3. Run the visualization and edge inference code on the pi using `python3 lsl_openbci.py` - - -## Sample Predictions - - - -Above is an example prediction for noisy real time ECG data obtained using the edge inference model. The beat predictions are represented as blue markers on the ECG. +# OMNI (Open Source Monitoring of Neonates and Infants) + + + + + +## Software Requirements +Run `sh requirements.sh` in a virtual environment in order to download the required libraries. + +## Hardware Requirements (Edge implementation) + + +### Components needed: + + 1. [OpenBCI Ganglion kit](https://shop.openbci.com/products/ganglion-board?variant=13461804483) + + Open source biosignal acquisition hardware for [research grade biosignal acquisition](https://openbci.com/community/published-research-with-openbci/) + + 2. [Raspberry Pi 4](https://www.raspberrypi.org/products/raspberry-pi-4-model-b/) + 3. [Rasberry Pi 4 cooling case](https://www.newegg.com/p/1W8-00Y1-00032) + 4. Shirt using [conductive textile electrodes](https://www.alibaba.com/product-detail/Conductive-textile-ecg-Electrodes_1127697682.html) [1$ for 1 electrode] + + +Wearable ECG electrodes --> OpenBCI Ganglion ---(Bluetooth)---> Raspberry Pi 4 + +## System Configuration +* Ubuntu 16.04 +* Nvidia 1080Ti - (Required for training the model) + +## Train a model to extract R peaks and Heart Rate from ECG waveform. +* Download ECG MITDB monitoring data from https://storage.googleapis.com/mitdb-1.0.0.physionet.org/mit-bih-arrhythmia-database-1.0.0.zip and unzip it. +* To train: `python train.py --preprocess_data --data_path "PATH TO DATA"` + +## Train a model to extract Breathing Rate from ECG waveform. +* Download ECG from the BIDMC database which is derived from MIMIC-II from https://physionet.org/static/published-projects/bidmc/bidmc-ppg-and-respiration-dataset-1.0.0.zip and unzip it. +* To train: `python train.py --preprocess_data --data_path "PATH TO DATA"` + +## Model Inference +* Download ECG from the preterm infant database from https://physionet.org/static/published-projects/picsdb/preterm-infant-cardio-respiratory-signals-database-1.0.0.zip and unzip it. +* Download the Heart Rate computation model from here: https://drive.google.com/open?id=1yI7G4nofjuzFWkD1CfsOtLZxaukTu0di +* Download the Breathing Rate computation model from here: https://drive.google.com/open?id=1ycV74LfGmgcGmLlrPn2VileeFNsGrRZT +* To run inference and view GUI type: `python run_model.py --path_dir "PATH TO DATA" --saved_hr_model_path "PATH TO HR MODEL" --saved_br_model_path "PATH TO BR MODEL" --patient_no 3 --viewer 1` + +# OMNI OpenBCI Pi Inference + + Edge inference of ECG R-peak detection and Respiration extraction using Raspberry Pi 4 using ECG (OpenBCI Ganglion). + + +## Hardware Design + +Wearable ECG electrodes --> OpenBCI Ganglion ---(Bluetooth)---> Raspberry Pi 4 + +## Software Design + +OpenBCI client ----(LSL)---> Python -> PyTorch inference --> Breathing Rate, Heart Rate + + +## Installation instruction + +### Install PyTorch on Raspberry Pi 4: + + 1. Install PyTorch dependicies + `sudo apt install libopenblas-dev libblas-dev m4 cmake cython python3-yaml libatlas-base-dev` + 2. Increase swap file memory to 1600, Edit variable `CONF_SWAPSIZE` in `/etc/dphys-swapfile` + 3. Reset environmental variables like ONNX_ML [Instructions](https://gist.github.com/akaanirban/621e63237e63bb169126b537d7a1d979) + 4. Download PyTorch package compiled for Armv7 ([torch-1.1.0-cp37-cp37m-linux_armv7l.whl](https://github.com/marcusvlc/pytorch-on-rpi/blob/master/torch-1.1.0-cp37-cp37m-linux_armv7l.whl)) + 5. Install using the command `sudo pip3 install torch-1.1.0-cp37-cp37m-linux_armv7l.whl` in the same directory + +Refer [here](https://github.com/marcusvlc/pytorch-on-rpi) for troubleshooting + +### Install OpenBCI Ganglion client on Raspberry Pi 4: + +1. Clone OpenBCI_Python repo + `git clone htps://github.com/OpenBCI/OpenBCI_Python.git` +2. Install the following requisites python packages using `pip3 install` + pylsl, python-osc, six, socketIO-client, websocket-client, Yapsy, xmldict, bluepy +3. Open folder OpenBCI_Python and run + `sudo python3 user.py --board ganglion -a streamer_lsl` to open a lab streaming layer stream of sensor data from the ganglion + +## Instructions to run to perform real time breathing rate/ heart rate inference using OpenBCI data +1. Run the lsl streamer script to get data to the inference script +`sudo python3 user.py --board ganglion -a streamer_lsl` +3. Run the visualization and edge inference code on the pi using `python3 lsl_openbci.py` + + +## Sample Predictions + + +Above is an example prediction for noisy real time ECG data obtained using the edge inference model. The beat predictions are represented as blue markers on the ECG.