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a888b0c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | # SPDX-License-Identifier: MIT
"""Self-contained HTF beat classifier for LTAF (N / A / V).
Three parallel streams (time + frequency + history) → fused → MLP head.
Inspired by alberto-rota/PAC-PVC-Beat-Classifier-for-ECGs (HTF ensemble).
Vendored from rmxjck/TSLM-Arena (src/models/ts_llm/ecg_beat_htf.py).
"""
from __future__ import annotations
from pathlib import Path
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
BEAT_CLASS_NAMES = ["N", "A", "V"]
class _ConvBlock(nn.Module):
def __init__(self, in_ch: int, out_ch: int, kernel: int = 7,
pool: int = 2, dropout: float = 0.0):
super().__init__()
self.conv = nn.Conv1d(in_ch, out_ch, kernel_size=kernel,
padding=kernel // 2, bias=False)
self.bn = nn.BatchNorm1d(out_ch)
self.act = nn.ReLU(inplace=True)
self.pool = nn.MaxPool1d(pool) if pool > 1 else nn.Identity()
self.drop = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.drop(self.pool(self.act(self.bn(self.conv(x)))))
class _CNNTrunk(nn.Module):
"""Stack of conv blocks ending in adaptive average pool."""
def __init__(self, in_channels: int, base_channels: int = 32,
n_blocks: int = 5, dropout: float = 0.1):
super().__init__()
layers = []
ch = in_channels
out_ch = base_channels
for _ in range(n_blocks):
layers.append(_ConvBlock(ch, out_ch, kernel=7, pool=2, dropout=dropout))
ch = out_ch
out_ch = min(out_ch * 2, 256)
self.net = nn.Sequential(*layers)
self.pool = nn.AdaptiveAvgPool1d(1)
self.out_channels = ch
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.pool(self.net(x)).squeeze(-1)
class EcgBeatHTFClassifier(nn.Module):
"""HTF ensemble: time + frequency + history → MLP head."""
def __init__(
self,
num_classes: int = 3,
class_names: List[str] = BEAT_CLASS_NAMES,
n_channels: int = 2,
window_samples: int = 256,
history_k: int = 5,
history_use_labels: bool = True,
time_base_channels: int = 32,
freq_base_channels: int = 32,
head_hidden: int = 128,
dropout: float = 0.1,
):
super().__init__()
assert len(class_names) == num_classes
self.num_classes = num_classes
self.class_names = list(class_names)
self.n_channels = n_channels
self.window_samples = window_samples
self.history_k = history_k
self.history_use_labels = history_use_labels
self.time_trunk = _CNNTrunk(n_channels, time_base_channels,
n_blocks=5, dropout=dropout)
self.freq_trunk = _CNNTrunk(n_channels, freq_base_channels,
n_blocks=4, dropout=dropout)
history_in = history_k
if history_use_labels:
history_in += history_k * num_classes
self.history_net = nn.Sequential(
nn.Linear(history_in, 64), nn.ReLU(inplace=True),
nn.Dropout(dropout),
nn.Linear(64, 64), nn.ReLU(inplace=True),
)
fused_dim = self.time_trunk.out_channels + self.freq_trunk.out_channels + 64
self.head = nn.Sequential(
nn.Linear(fused_dim, head_hidden),
nn.ReLU(inplace=True),
nn.Dropout(dropout),
nn.Linear(head_hidden, num_classes),
)
def _compute_freq(self, x: torch.Tensor) -> torch.Tensor:
spec = torch.fft.rfft(x, dim=-1)
return torch.log1p(spec.abs())
def forward(
self,
x_time: torch.Tensor,
rr_history: torch.Tensor,
label_history: torch.Tensor | None = None,
) -> torch.Tensor:
"""
Args:
x_time: (B, 2, 256) — raw 2-lead ECG window centered on R-peak.
rr_history: (B, K) — RR intervals to preceding K beats, in
seconds. Set 0.0 for missing (record start).
label_history: (B, K) int64 — preceding K beat labels (0=N, 1=A,
2=V). Use -1 for missing. Used iff `history_use_labels=True`.
At inference time, can be filled with previous predictions
(autoregressive) or zeros (recall slightly degrades).
Returns:
(B, num_classes) logits.
"""
time_feat = self.time_trunk(x_time)
freq_feat = self.freq_trunk(self._compute_freq(x_time))
if self.history_use_labels:
assert label_history is not None
B, K = label_history.shape
valid = (label_history >= 0).float().unsqueeze(-1)
idx = label_history.clamp(min=0)
one_hot = F.one_hot(idx, num_classes=self.num_classes).float() * valid
hist_in = torch.cat([rr_history, one_hot.reshape(B, -1)], dim=-1)
else:
hist_in = rr_history
hist_feat = self.history_net(hist_in)
fused = torch.cat([time_feat, freq_feat, hist_feat], dim=-1)
return self.head(fused)
@classmethod
def load(cls, path: str | Path, device: str = "cpu") -> "EcgBeatHTFClassifier":
ckpt = torch.load(path, map_location=device, weights_only=False)
model = cls(
num_classes=ckpt["num_classes"],
class_names=ckpt["class_names"],
n_channels=ckpt["n_channels"],
window_samples=ckpt["window_samples"],
history_k=ckpt["history_k"],
history_use_labels=ckpt["history_use_labels"],
)
model.load_state_dict(ckpt["state_dict"])
model.to(device).eval()
return model
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