report.Rmd

---
title: "Report"
author: "Francisco Bischoff"
date: "on `r format(Sys.time(), '%B %d, %Y')`"
output:
  workflowr::wflow_html:
    number_sections: true
    fig_caption: yes
    toc: false
bibliography: ../papers/references.bib
link-citations: true
# Download your specific csl file and refer to it in the line below.
csl: https://raw.githubusercontent.com/citation-style-language/styles/master/american-medical-association.csl
---

```{=html}
<style>
div.polaroid {
  width: 80%;
  height: auto;
  margin-left: auto;
  margin-right: auto;
  background-color: white;
  box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);
}

svg.svglite {
  width: 100%;
  height: auto;
}

div.polcont {
  text-align: center;
  padding-top: 15px;
  font-weight: bold;
}
div.polaroid {
  width: 80%;
  height: auto;
  margin-left: auto;
  margin-right: auto;
  background-color: white;
  box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);
}
div.figure {
  padding: 10px;
  margin: 20px 0px 20px 0px;
  background-color: white;
  box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);
}
p.caption {
  font-size: 19px;
}
</style>
```
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, fig.align = "center")
library(here)
library(visNetwork)
library(tibble)
library(kableExtra)
library(targets)
knitr::opts_knit$set(root.dir = here("docs"), base.dir = here("docs"))
```

# Current Work Status

## Principles

This research is being conducted using the Research Compendium principles [@compendium2019]:

1.  Stick with the convention of your peers;
2.  Keep data, methods, and output separated;
3.  Specify your computational environment as clearly as you can.

Data management is following the FAIR principle (findable, accessible, interoperable, reusable)
[@wilkinson2016].

## The data

The current dataset used is the CinC/Physionet Challenge 2015 public dataset, modified to include
only the actual data and the header files in order to be read by the pipeline and is hosted by
Zenodo [@bischoff2021] under the same license as Physionet.

The dataset is composed of 750 patients with at least five minutes records. All signals have been
resampled (using anti-alias filters) to 12 bit, 250 Hz and have had FIR bandpass (0.05 to 40Hz) and
mains notch filters applied to remove noise. Pacemaker and other artifacts still present on the ECG
[@Clifford2015]. Furthermore, this dataset contains at least two ECG derivations and one or more
variables like arterial blood pressure, photoplethysmograph readings, and respiration movements.

The *event* we seek to improve is the detection of a life-threatening arrhythmia as defined by
Physionet in Table \ref{tab:alarms}.

```{r alarms, echo=FALSE}
alarms <- tribble(
  ~Alarm, ~Definition,
  "Asystole", "No QRS for at least 4 seconds",
  "Extreme Bradycardia", "Heart rate lower than 40 bpm for 5 consecutive beats",
  "Extreme Tachycardia", "Heart rate higher than 140 bpm for 17 consecutive beats",
  "Ventricular Tachycardia", "5 or more ventricular beats with heart rate higher than 100 bpm",
  "Ventricular Flutter/Fibrillation", "Fibrillatory, flutter, or oscillatory waveform for at least 4 seconds"
)

kbl(alarms, booktabs = TRUE, caption = "Definition of the five alarm types used in CinC/Physionet Challenge 2015 challenge.", align = "ll") %>%
  kable_styling(full_width = TRUE) %>%
  # column_spec(c(1,2), width = "50px") %>%
  row_spec(0, bold = TRUE)
```

The fifth minute is precisely where the alarm has been triggered on the original recording set. To
meet the ANSI/AAMI EC13 Cardiac Monitor Standards [@AAMI2002], the onset of the event is within 10
seconds of the alarm (i.e., between 4:50 and 5:00 of the record). That doesn't mean that there are
no other arrhythmias before, but those were not labeled.

## Workflow

All steps of the process are being managed using the R package `targets` [@landau2021] from data
extraction to the final report, as shown in Fig. \ref{fig:targets}.

```{r targets, echo=FALSE, out.width="100%", fig.cap="Reproducible research workflow using `targets`."}
knitr::include_graphics("figure/targets.png")
```

The report is available on the main webpage [@franz_website], allowing inspection of previous
versions managed by the R package `workflowr`[@workflowr2021], as shown in Fig.
\ref{fig:workflowr}.

```{r workflowr, echo=FALSE, out.width="100%", fig.cap="Reproducible reports using `workflowr`."}
knitr::include_graphics("figure/workflowr_print.png")
```

## Work in Progress

### Project start

The project started with a literature survey on the databases Scopus, Pubmed, Web of Science, and
Google Scholar with the following query (the syntax was adapted for each database):

    TITLE-ABS-KEY ( algorithm  OR  'point of care'  OR  'signal processing'  OR  'computer assisted'  OR  'support vector machine'  OR  'decision support system*'  OR  'neural network*'  OR  'automatic interpretation' OR 'machine learning')  AND  TITLE-ABS-KEY ( electrocardiography  OR  cardiography  OR  'electrocardiographic tracing'  OR  ecg OR  electrocardiogram  OR  cardiogram )  AND  TITLE-ABS-KEY ( 'Intensive care unit' OR 'cardiologic care unit'  OR  'intensive care center'  OR  'cardiologic care center' )

\

The inclusion and exclusion criteria were defined as in Table \ref{tab:criteria}.

```{r criteria, echo=FALSE}
criteria <- tribble(
  ~"Inclusion criteria", ~"Exclusion criteria",
  "ECG automatic interpretation", "Manual interpretation",
  "ECG anomaly detection", "Publication older than ten years",
  "ECG context change detection", "Do not attempt to identify life-threatening arrhythmias, namely asystole, extreme bradycardia, extreme tachycardia, ventricular tachycardia, and ventricular flutter/fibrillation",
  "Online Stream ECG analysis", "No performance measurements reported",
  "Specific diagnosis (like a flutter, hyperkalemia, etc.)", ""
)

kbl(criteria, booktabs = TRUE, caption = "Literature review criteria.", align = "ll") %>%
  kable_styling(full_width = TRUE) %>%
  column_spec(1, width = "10cm") %>%
  row_spec(0, bold = TRUE)
```

The current stage of the review is on Data Extraction, from the resulting screening shown in Fig.
\ref{fig:prisma}.

```{r prisma, echo=FALSE, out.width="80%", fig.cap="Prisma results"}
knitr::include_graphics("figure/PRISMA.png")
```

Meanwhile, the project pipeline has been set up on GitHub, Inc. [@bischoffrepo2021] leveraging on
Github A ions [@gitactions2021] for the Continuous Integration lifecycle, the repository is
available at [@bischoffrepo2021], and the resulting report is available at [@franz_website] for
transparency while the roadmap and tasks are managed using the integrated Zenhub [@zenhub2021].

As it is known worldwide, 2020 was hard on every project, which required changes on the timeline. In
Fig. \ref{fig:zenhub1} it is shown the initial roadmap (as of May 2020) and Fig. \ref{fig:zenhub2}
the modified roadmap (as of July 2021).

```{r zenhub1, echo=FALSE, out.width="100%", fig.cap="Roadmap original"}
knitr::include_graphics("figure/roadmap_original.png")
```

```{r zenhub2, echo=FALSE, out.width="100%", fig.cap="Roadmap updated"}
knitr::include_graphics("figure/roadmap_updated.png")
```

### Preliminary Experimentations

**RAW Data**

While programming the pipeline for the current dataset, it has been acquired a Single Lead Heart
Rate Monitor breakout from Sparkfun^TM^ [@sparkfun2021] using the AD8232 [@AnalogDevices2020]
microchip from Analog Devices Inc., compatible with Arduino^(R)^ [@arduino2021], for an in-house
experiment (Figs. \ref{fig:ad8232} and \ref{fig:fullsetup}).

```{r ad8232, echo=FALSE, out.width="50%", fig.cap="Single Lead Heart Rate Monitor"}
knitr::include_graphics("figure/sparkfun.jpg")
```

```{r fullsetup, echo=FALSE, out.width="50%", fig.cap="Single Lead Heart Rate Monitor"}
knitr::include_graphics("figure/FullSetup.jpg")
```

The output gives us a RAW signal as shown in Fig. \ref{fig:rawsignal}.

```{r rawsignal, echo=FALSE, out.width="50%", fig.cap="RAW output from Arduino at ~300hz"}
knitr::include_graphics("figure/arduino_plot.jpg")
```

After applying the same settings as the Physionet database (collecting the data at 500hz, resample
to 250hz, pass-filter, and notch filter), the signal is much better as shown in Fig.
\ref{fig:filtersignal}. Note: the leads were not placed on the correct location.

```{r filtersignal, echo=FALSE, out.width="100%", fig.cap="Gray is RAW, Red is filtered"}
knitr::include_graphics("figure/filtered_ecg.png")
```

So in this way, we allow us to import RAW data from other devices and build our own test dataset in
the future.

**Detecting Regime Changes**

The regime change approach will be using the *Arc Counts*, as explained elsewhere [@gharghabi2018].
The current implementation of the Matrix Profile in R, maintained by the first author of this
thesis, is being used to accomplish the computations. This package was published in R Journal
[@RJ-2020-021].

A new concept was needed to be implemented on the algorithm in order to emulate (in this first
iteration) the behavior of the real-time sensor: the search must only look for previous information
within a time constraint. Thus, both the Matrix Profile computation and the *Arc Counts* needed to
be adapted for this task.

At the same time, the ECG data needs to be "cleaned" for proper evaluation. That is different from
the initial filtering process. Several SQIs (Signal Quality Indexes) are used on literature
[@eerikainen2015], some trivial measures as *kurtosis*, *skewness*, median local noise level, other
more complex as pcaSQI (the ratio of the sum of the five largest eigenvalues associated with the
principal components over the sum of all eigenvalues obtained by principal component analysis
applied to the time aligned ECG segments in the window). By experimentation (yet to be validated), a
simple formula gives us the "complexity" of the signal and correlates well with the noisy data is
shown in Equation (1).

$$
\sqrt{\sum_{i=1}^w((x_{i+1}-x_i)^2)}, \quad \text{where}\; w \; \text{is the window size} \tag{1}
$$

\
\
The Fig. \ref{fig:sqi} shows some SQIs.

```{r sqi, echo=FALSE, out.width="100%", fig.cap="Green line is the \"complexity\" of the signal"}
knitr::include_graphics("figure/noise.png")
```

Finally, a sample of the regime change detection is shown in Figs. \ref{fig:regimefilter} to
\ref{fig:regimetrue}.

Fig. \ref{fig:regimefilter} shows that noisy data (probably patient muscle movements) are marked
with a blue point and thus are ignored by the algorithm. Also, valid for the following plots, the
green and red lines on the data mark the 10 seconds window where the "event" that triggers the alarm
is supposed to happen.

```{r regimefilter, echo=FALSE, out.width="100%", fig.cap="Regime changes with noisy data - false alarm"}
knitr::include_graphics("figure/regime_filter.png")
```

In Fig. \ref{fig:regimefalse}, the data is clean; thus, nothing is excluded. Interestingly one of
the detected regime changes is inside the "green-red" window. But it is a false alarm.

```{r regimefalse, echo=FALSE, out.width="100%", fig.cap="Regime changes with good data - false alarm"}
knitr::include_graphics("figure/regime_false.png")
```

The last plot (Fig. \ref{fig:regimetrue}) shows the algorithm's robustness, not excluding good data
with a wandering baseline, and the last regime change is correctly detected inside the "green-red"
window.

```{r regimetrue, echo=FALSE, out.width="100%", fig.cap="Regime changes with good but wandering data - true alarm"}
knitr::include_graphics("figure/regime_true.png")
```


```{r load_target, results='asis'}

# fluss_plots <- tar_read("fluss_plots")
#
# files <- names(fluss_plots)
# checkmate::qassert(files, "S")
#
# for (f in files) {
#   alarm <- attr(fluss_plots[[f]], "info")$alarm
#   true <- attr(fluss_plots[[f]], "info")$true
#
#   series <- names(fluss_plots[[f]])
#   series <- setdiff(series, c("time", "ABP", "PLETH", "RESP"))
#
#
#   for (s in series) {
#
#     svg <- attr(fluss_plots[[f]][[s]], "plot")
#
#     cat("<div class=\"polaroid\">\n")
#     cat("<div class=\"polcont\">\n")
#     cat(sprintf("File: %s - \n", f))
#     cat(sprintf("Alarm: %s - \n", alarm))
#     cat(sprintf("True alarm: %s - \n", true))
#     cat(sprintf("Data stream: %s\n", s))
#     cat("</div>\n")
#     cat(svg)
#     cat("</div><br/>\n")
#   }
#
#   cat("<br/>\n")
# }
```